Hey there I engineer the software solutions

I'm Khaqan Ashraf, a Senior Software Engineer and Data Scientist.

• MS in Data Science
  Information Technology University Lahore

• BS in Computer Science
  Government College University Lahore

• Systems Ltd | Pakistan, Lahore | April 2024 - Present
• Turing | Remote | September 2024 - September 2025
• Tkxel | Pakistan, Lahore | July 2022 - March 2024
• Tossdown | Pakistan, Lahore | November 2020 - June 2022

• Abstract:
  Crops are a vital part of the economy and livelihood, making crop protection crucial. In this research, we explored the application of deep learning techniques for automating the detection of pests in crops. Using the IP102 dataset and ensemble pre-trained deep CNN models, we developed a weighted ensemble technique with learnable parameters. Additionally, we utilized pretrained vision transformers to classify stem rust, leaf rust, and healthy wheat from a small dataset sourced from the ICLR challenge. Through experimentation with CNN architectures and ViT models, we demonstrated the effectiveness of large-scale pretrained vision transformers on small datasets, outperforming state-of-the-art CNN architectures. Our research highlights the importance of leveraging pretrained models and the transferability of features learned from ImageNet21 in agricultural applications.

• Key Contributions

  • Developed a weighted ensemble technique with learnable parameters for pest detection.
  • Demonstrated the effectiveness of pretrained vision transformers on small agricultural datasets.
  • Achieved competitive performance in the ICLR challenge through the use of ViT models pretrained on ImageNet21.

• Learnings

  • Pretrained vision transformers are effective for small datasets in agricultural contexts.
  • Features learned from ImageNet21 are transferable to diverse datasets, such as wheat plant images.

• Description:
  The Stealth25519 Python package provides functionality for the generation, verification, and signing of stealth addresses using the standard ed25519 algorithm with the ECDH protocol.

• Features

  • The sender can generate a stealth address using the public view and the spend key of the recipient.
  • The recipient can use their public spend key and private view key to identify transactions intended for them.
  • The recipient can sign transactions for all transactions that belong to them.
  • The signature generated by the recipient can be verified by anyone with the stealth address public key.

• Code Example

  from stealth25519.generator import StealthAddressGenerator
  
  public_spend_key_bytes = bytes.fromhex('18a498c68461e39dd180745e5aa1faacbc9b8a5f74a7eb25b5038b66db0a4af6')
  public_view_key_bytes = bytes.fromhex('b52c33b513c26e17b7105cb1ed1c7022ef00f3967aaac0ff8bd9d15ccee4d94e')
  
  public_spend_key = PublicKey(public_spend_key_bytes)
  public_view_key = PublicKey(public_view_key_bytes)
  
  generator = StealthAddressGenerator(public_spend_key, public_view_key, hash_function = sha512)
  stealth_address = generator.generate()
  print('Stealth Address\n', stealth_address)

• Documentation: stealth25519

• Source code: stealth-py

• Function-Calling and Data Extraction with LLMs | DeepLearning.AI 2024
• Data Engineer Certificate | Mangtas 2023
• Machine Learning, Data Science, and Deep Learning with Python | Udemy 2022

As an LLM Trainer, I specialize in designing, refining, and aligning large language models (LLMs) for safe, accurate, and context-aware outputs. My work bridges machine learning and software engineering, focusing on training models to solve domain-specific challenges (e.g., code generation, audio/visual understanding) while mitigating risks like harmful outputs or inefficiencies. Refer to the LLM capabilities section.

As a Data Scientist, I partner with businesses to identify growth opportunities through machine learning. My work revolves around analyzing existing workflows, designing tailored ML solutions, and ensuring seamless integration of models into production systems—all while balancing technical feasibility, cost efficiency, and business impact. Refer to the LLM capabilities section.

As a versatile Full-Stack Software Engineer, I specialize in architecting and delivering high-performance, scalable solutions across diverse domains—from business intelligence platforms and telecom systems to blockchain-integrated metaverse games and AI/ML-driven tools. My expertise spans backend development (Node.js, Python, PHP), RESTful API design, database optimization (SQL, NoSQL), and cloud-native deployment (AWS, Docker). I’ve led end-to-end development of enterprise-grade applications, implementing MVC architectures, securing systems with JWT authentication, and optimizing performance through caching (Redis), parallel processing, and efficient query design. My work emphasizes scalability (microservices, monorepos), reliability (transaction management, CI/CD pipelines), and innovation (blockchain integration, LLM-driven tools), while solving complex challenges like high-concurrency data workflows, third-party API latency, and dynamic system scaling. I bridge technical rigor with business impact, ensuring solutions are both robust and aligned with user needs. Please refer to the projects section.

As a Blockchain Developer, I design, deploy, and integrate secure smart contracts into scalable systems, bridging decentralized technologies with traditional backend architectures. My work spans building blockchain-powered features for metaverse games (e.g., Nitro League) and maintaining a disciplined focus on continuous learning through challenges like the 100 Days of Blockchain commitment. Also, please refere to the Nitro League and Stealth Address Library project and Python package for stealth addresses

Developed a specialized LLM precision-tuned to generate, analyze, and correct Python code in response to user queries. As an LLM Trainer, I evaluated model outputs for accuracy, security, and efficiency, authored gold-standard code responses, and iteratively refined training datasets. Key contributions included identifying edge cases (e.g., recursion errors, insecure dependencies), implementing adversarial testing, and aligning outputs with real-world programming challenges to ensure robust, error-free code generation. The model now assists developers in writing secure, optimized Python scripts across diverse use cases.

Trained an LLM to generate, debug, and optimize JavaScript code for diverse use cases (web apps, APIs, Node.js). As an LLM Trainer, I curated datasets of real-world JS scenarios, authored high-quality code samples, and iteratively refined model outputs for accuracy and security. Key contributions included identifying and correcting edge cases (e.g., async/await pitfalls, callback hell), mitigating vulnerabilities (XSS, prototype pollution), and optimizing code efficiency (memory leaks, event-loop bottlenecks). The model now delivers production-ready JavaScript solutions aligned with modern best practices.

Spearheaded adversarial testing of a multimodal LLM to identify vulnerabilities in code generation triggered by image-based prompts. As an AI Security Specialist, I designed adversarial attacks by crafting deceptive prompts (e.g., generating malicious code snippets, unethical coding practices) from images, stress-testing the model’s alignment safeguards. Responsibilities included curating image-to-prompt attack vectors, analyzing harmful outputs (malware, SQL injection), and refining SFT datasets to mitigate risks. The project hardened the model’s resilience against coding-domain exploits, ensuring ethical and secure code generation.

Designed and executed a supervised fine-tuning (SFT) pipeline to train an LLM for multimodal audio tasks, including speech understanding, generation, and noise-resilient processing. As an Audio AI Trainer, I engineered text-based prompt-response pairs simulating real-world audio challenges (background noise, emotional tone variations, interruptions) to teach the model context-aware responses. Collaborated with audio engineers to convert these scenarios into synthetic training data (speech clips, sound effects), enabling the model to robustly parse and generate audio even in suboptimal conditions. Key outcomes included improved performance in noisy environments and nuanced paralinguistic comprehension.

Engineered an SFT pipeline to train an LLM in interpreting visual inputs and autonomously invoking external APIs/tools via structured JSON calls. As an AI Trainer, I designed multimodal prompts combining images (e.g., graphs, invoices, maps) with user queries, teaching the model to extract visual data (numbers, locations) and trigger context-aware tool usage (payment processing, weather APIs, live data fetching). Responsibilities included curating function schemas, validating JSON outputs against image context, and refining the model’s ability to chain API responses into coherent final answers. The system now solves complex, real-time tasks (e.g., “Calculate shipping costs from this warehouse photo”) beyond standard LLM capabilities.

• Description
  Lightweight, embeddable web micro-apps (vanilla JavaScript) integrated into a mobile app to capture, validate, and manage user locations and national address workflows. Fully token-secured and localized (Arabic/English), with runtime configuration loaded from a server-hosted JS file.

• Roles & Responsibilities

  • Implemented front-end micro-apps and map UX flows (geo-location, search, pin placement).
  • Integrated runtime config fetching and secure token propagation to backend APIs.
  • Built client-side validation and multi-step data capture (accommodation type, period, etc.).
  • Implemented i18n and RTL/LTR UI adjustments and coordinated API contracts with the backend.

• Challenges Faced

  1. Secure Token Propagation
     Challenge: The host app must supply a user token to the micro-apps.
     Solution: Designed a small token handshake pattern where the host injects the token into the micro-app container, and every API call includes the token in the Authorization headers.
  2. Ensuring Data Consistency Across Complex Workflows
     Challenge: Concurrent data updates and distributed transactions risked inconsistencies.
     Solution: Implemented database transactions with SQLAlchemy’s session management and added retry logic for failed operations.
  3. Localization & RTL Layout for Arabic
     Challenge: The UI and flows needed to behave correctly in RTL.
     Solution: Separated string resources, used direction-aware CSS classes, and tested copy/UX in both languages.
  4. Config Drift & Dynamic Endpoints
     Challenge: Endpoints/configs could change without rebuilding the apps.
     Solution: Loaded API endpoints and feature flags from a server-hosted JS config at runtime with sensible fallbacks and version checks.
  5. Accurate Address Capture on Mobile
     Challenge: Device geo-location and address validation varied by device/provider.
     Solution: Combined browser geo-location, reverse geocoding, and client-side validation with a user confirmation step before submission.

• Description
  This comprehensive end-to-end test automation framework, built with Playwright, is designed for a modern web application. The framework ensures application quality by running tests across multiple environments (QA, staging, Production). It features a scalable architecture, supports cross-browser and mobile device testing, and provides detailed test reports through Allure integration.

• Roles & Responsibilities

  • Framework Architect & Developer: Designed and implemented a robust test automation framework from the ground up using Playwright and JavaScript, following industry best practices.
  • Test Case Automation: Authored and maintained a suite of automated test scripts for critical application features, covering smoke, regression, and end-to-end testing scenarios.
  • Environment Management: Established and managed configurations for multiple test environments (QA, Staging, Prod) using environment variables, ensuring consistent and reliable test execution.
  • CI/CD Integration: Set up and configured the test suite to run in a CI/CD pipeline (Bitbucket Pipelines), enabling automated testing on every code change.
  • Reporting & Analysis: Integrated the Allure framework to generate detailed, interactive, and easy-to-understand test reports, helping stakeholders quickly assess the quality of the application.
  • Code Quality & Maintainability: Implemented the Page Object Model (POM) to create a clean, maintainable, and reusable test codebase.

• Challenges Faced

  1. Managing Test Data for Different Environments
     Challenge: The application required different sets of test data for QA, Staging, and Production environments. Hardcoding data would have made the tests brittle and difficult to maintain. Solution: I implemented a data-driven approach using JSON files (NGCA.qa.json, NGCA.prod.json) for each environment. A global setup script dynamically loads the appropriate test data based on the target environment, making the tests flexible and easy to manage.
  2. Ensuring Consistent Test Execution Across Environments Challenge: Running tests on different environments with varying configurations (e.g., base URLs, credentials) manually was error-prone and inefficient. Solution: I utilized cross-env and .env files to manage environment-specific variables. This allowed for seamless execution of tests against different environments with a single command (e.g., npm run test:qa), improving reliability and efficiency.
  3. Lack of Detailed and Actionable Test Reports Challenge: The default test reports were not providing enough insight into test failures, making it difficult to debug and analyze issues. Solution: I integrated the Allure reporting tool with Playwright. This provided rich, interactive reports with screenshots, videos on failure, step-by-step execution details, and historical data, which significantly improved the debugging process and provided clear visibility into test results for the entire team.

• Description
  Developed a business intelligence web application for a retail organization to analyze and compare sales performance across brands, stores, and regions. The application enables managers to track historical sales data (from previous years) and input current monthly/yearly sales figures, providing actionable insights into growth trends, competitor benchmarking, and store-level performance.

• Role and Responsibilities

  • Designed sequence diagrams to model application workflows and ensure clarity in service interactions.
  • Structured the application using MVC architecture, separating models (SQLAlchemy), controllers (business logic), and views (API endpoints).
  • Built RESTful APIs to handle CRUD operations, user authentication, and data processing tasks.
  • Implemented JWT authentication and authorization to secure endpoints and manage user roles.
  • Processed and transformed large datasets using pandas DataFrames for analytics and reporting.
  • Integrated caching mechanisms (e.g., Redis) to store frequently accessed data, reducing database load by 30%.
  • Utilized SQLAlchemy ORM to define models, execute complex queries, and ensure database-agnostic operations.
  • Enforced data consistency through transaction management, database constraints, and atomic operations.
  • Developed isolated controllers to decouple business logic from data access layers, improving code testability.
  • Containerized the application using Docker and orchestrated multi-container environments for development and production.
  • Wrote unit and integration tests using pytest to achieve 95% code coverage and validate API reliability.
  • Deployed the application with WSGI (Gunicorn) and Nginx to ensure high performance under load.

• Challenges Faced

  1. Ensuring Data Consistency Across Complex Workflows
     Challenge: Concurrent data updates and distributed transactions risked inconsistencies.
     Solution: Implemented database transactions with SQLAlchemy’s session management and added retry logic for failed operations.

  2. Efficient Processing of Large Datasets
     Challenge: In-memory data processing with DataFrames caused performance bottlenecks.
     Solution: Optimized DataFrame operations using chunking, indexing, and lazy loading, reducing memory usage by 25%.

  3. Testing Database-Dependent Workflows
     Challenge: Complex database interactions made tests slow and difficult to isolate.
     Solution: Leveraged pytest fixtures to mock SQLAlchemy sessions and create ephemeral test databases.

• Description
  Developed a high-performance backend system for a telecom company’s mobile application, enabling users to manage telecom services seamlessly. The application supported critical functionalities such as SIM card ordering, prepaid/postpaid package purchases, wallet-based transactions, subscription management (add-ons, unsubscribing), and real-time due deductions. Designed to handle high concurrent traffic, the system integrated with payment gateways, telecom infrastructure APIs, and notification services while ensuring security, scalability, and reliability. Built using Node.js in a monorepo (managed via Nx Console), it employed Docker for containerization, Redis for caching, and MySQL for transactional data storage. Key features included PDF invoice generation, email notifications, and role-based access control.

• Role and Responsibilities

  • Designed and developed RESTful APIs to support user workflows: SIM ordering, package selection, wallet transactions, and subscription management.
  • Integrated third-party APIs (e.g., payment gateways, SMS services, telecom provisioning systems) to validate transactions, activate services, and sync user data.
  • Secured endpoints using JWT-based authentication and Passport.js strategies, enforcing role-based access for users and admin roles.
  • Implemented the dependency injection design pattern to decouple service logic, enhancing testability and scalability across modules.
  • Built a modular monorepo with Nx Console to manage shared libraries, utilities, and microservices efficiently.
  • Developed an email service using SendGrid to notify users about order confirmations, payment receipts, and subscription updates.
  • Automated PDF invoice generation from HTML templates using Puppeteer, ensuring consistent branding and dynamic data rendering.
  • Optimized performance using Redis to cache frequently accessed data (e.g., package details, user balances) and rate-limit API requests.
  • Dockerized services for consistent deployment and orchestrated cloud storage of invoices/assets using AWS S3.
  • Wrote comprehensive test suites with Mocha to validate API logic, edge cases, and integration workflows.

• Challenges Faced

  1. High Latency in Third-Party API Integrations Challenge: Frequent delays and timeouts when interacting with external APIs affected user experience.
     Solution: Implemented Redis caching for frequently accessed data and optimized parallel API calls to reduce response times by 40%.

  2. Testing Complex Service Dependencies Challenge: Testing interdependent services in a monorepo led to flaky tests and false positives.
     Solution: Isolated test environments using Mocha hooks and Nx Console’s dependency graph to run targeted tests, ensuring reliability.

  3. Resource-Intensive PDF Generation Challenge: Generating PDFs from dynamic HTML templates caused server bottlenecks.
     Solution: Offloaded PDF rendering to a dedicated service using Puppeteer and optimized HTML templates with precompiled layouts.

• Description
  Nitro is a metaverse game project integrating blockchain technology. It features a Node.js backend with TypeScript and DynamoDB for data storage. The frontend is developed using React.js, and deployment is managed using Docker containers on AWS EC2 instances and Lambda functions.

• Role and Responsibilities
  As the lead software engineer, my responsibilities included designing the backend architecture, implementing new features, and providing support for existing features.

• Challenges Faced

  1. Slow Query Performance with Dynamodb
     Challenge: Initial queries to DynamoDB were slow, impacting overall application performance.
     Solution: Conducted a comprehensive analysis of the query patterns and identified inefficient scan operations. I then restructured the data model to better align with access patterns, created composite indexes to support complex queries, and optimized the use of partition keys to evenly distribute the data load. Implemented batch operations to reduce the number of read requests and improve throughput.

  2. High Latency in Asset Retrieval from the Blockchain
     Challenge: Retrieving assets from the blockchain was slow, causing delays in game interactions.
     Solution: Implemented a caching layer using Redis to store frequently accessed assets, significantly reducing retrieval times. Additionally, I set up asynchronous processes to prefetch and update the cache with the latest assets, ensuring that the most current data was available with minimal delay. Employed background jobs using AWS Lambda functions to handle periodic asset updates.

  3. Scalability Issues with Backend Services
     Challenge: The backend services experienced performance bottlenecks under high user load.
     Solution: Designed and implemented a microservices architecture to distribute the load across multiple services. Used Docker containers to isolate and scale services independently. Implemented auto-scaling groups on AWS EC2 instances to dynamically adjust resources based on traffic. Enhanced inter-service communication using AWS SQS to manage message queues and ensure reliable data exchange.

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• Description
  WhatsApp Web Nativefier Linux App is a straightforward application that utilizes Nativefier to package WhatsApp Web as a native desktop application for Linux. By wrapping WhatsApp Web in a dedicated browser window with Nativefier, the app provides a seamless, standalone experience on Linux, mimicking a native application’s look and feel while maintaining the web-based functionality of WhatsApp.

• Role and Responsibilities
  As the primary developer, my responsibilities included setting up and configuring Nativefier to create a dedicated application window for WhatsApp Web. I managed the customization of the app’s appearance and functionality to ensure an optimal user experience. This included configuring the app settings, testing across different Linux distributions, and addressing any compatibility issues.

• Challenges Faced

  1. Customization of Nativefier Output
     Challenge: Achieving the desired appearance and functionality of the application through Nativefier’s default settings.
     Solution: Customized the Nativefier build by modifying configuration options to adjust the window size, icon, and other visual aspects. Implemented additional scripts to handle specific user interface preferences and ensured that the application adhered to the visual standards expected from a native desktop app.

  2. Performance Optimization
     Challenge: Maintaining responsive performance while running WhatsApp Web within a Nativefier-generated application.
     Solution: Implemented a caching layer using Redis to store frequently accessed assets, significantly reducing retrieval times. Additionally, I set up asynchronous processes to prefetch and update the cache with the latest assets, ensuring that the most current data was available with minimal delay. Employed background jobs using AWS Lambda functions to handle periodic asset updates.

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• Description
  TechPurview is a society management system built with a Node.js backend and PostgreSQL database. The frontend is developed using Next.js, and the application is deployed on AWS EC2 instances using Docker containers.enhancements.

• Role and Responsibilities
  As the lead software engineer, my responsibilites including the architecting the backend infrastructure to develop, integrate, deploy and delivering the complete proejct.

• Challenges Faced

  1. Managing Multiple Connections to the Database
     Challenge: Handling multiple connections to the PostgreSQL database led to potential performance issues and resource wastage.
     Solution: Implemented the singleton design pattern to ensure that only one instance of the database connection is initiated and served for all requests. This optimized resource usage and improved overall system performance.

  2. Ensuring Data Consistency and Integrity
     Challenge: Maintaining data consistency and integrity across multiple transactions was challenging, especially with concurrent database operations.
     Solution: Implemented transaction management using PostgreSQL’s ACID properties to ensure data consistency and integrity. Used connection pooling to manage concurrent connections efficiently and avoid deadlocks. Applied proper indexing and optimized SQL queries to enhance database performance.

  3. Optimizing Query Performance
     Challenge: Some complex queries were slow, impacting the overall responsiveness of the system.
     Solution: Conducted query performance analysis and optimization. Created necessary indexes to speed up frequently used queries. Refactored and optimized complex queries to reduce execution time. Implemented caching strategies using Redis to store the results of frequently accessed data, thereby reducing database load.

  4. Handling Session Management Securely
     Challenge: Managing user sessions securely to prevent unauthorized access and ensure data privacy.
     Solution: Implemented secure session management using JWT (JSON Web Tokens) for authentication. Ensured that JWTs were securely signed and stored. Used HTTPS for all communication to protect data in transit. Regularly reviewed and updated security protocols to mitigate potential vulnerabilities.

  5. Coordinating Backend and Frontend Development
     Challenge: Ensuring seamless integration between the backend and frontend components, and maintaining consistent data flow.
     Solution: Established clear communication protocols and API documentation to ensure that the backend services met the frontend requirements. Used tools like Swagger for API documentation and Postman for testing. Conducted regular integration testing and code reviews to ensure smooth and efficient collaboration between the backend and frontend teams.

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• Description
  Tossdown is a multivendor ecommerce engine developed using Node.js, CodeIgniter (PHP framework), and MySQL database. The backend is primarily implemented in Node.js, while certain functionalities are handled by serverless Lambda functions.

• Role and Responsibilities
  As a senior software engineer on the Tossdown project, my responsibilities included optimizing performance, analyzing database queries, and implementing search functionalities.

• Challenges Faced

  1. Slow Performance of Certain Endpoints
     Challenge: Certain API endpoints were slow, impacting user experience and overall system efficiency.
     Solution: Identified endpoints with poor performance by analyzing database queries and code execution. Used the "EXPLAIN" keyword to understand query execution plans and identify bottlenecks. Optimized MySQL queries by adding appropriate indexes, refactoring complex joins, and removing unnecessary iterations. Implemented caching strategies using Redis to store frequently accessed data, reducing the need for repetitive database queries.

  2. Redundant Search Results Affecting Search Accuracy and Relevance
     Challenge: Search results were often redundant and not accurately relevant to user queries.
     Solution: Implemented a solution to periodically move data from MySQL to Elasticsearch, ensuring that product data is indexed and searchable with full-text search capabilities. This approach improved search accuracy and reduced redundant search results. Additionally, fine-tuned the Elasticsearch queries to include filtering, boosting, and sorting to enhance search relevance and user satisfaction.

  3. Handling High Traffic Loads and Ensuring Scalability
     Challenge: The system needed to handle high traffic loads, especially during peak times, without degrading performance.
     Solution: Designed and implemented a scalable architecture using AWS services. Deployed backend services on AWS EC2 instances with auto-scaling groups to automatically adjust the number of instances based on traffic. Used AWS Lambda functions for certain functionalities to ensure efficient and scalable execution of tasks. Implemented a load balancer to distribute incoming requests evenly across instances, ensuring high availability and reliability.

  4. Maintaining Data Consistency Between MySQL and Elasticsearch
     Challenge: Ensuring that data remains consistent between MySQL and Elasticsearch during updates and deletions.
     Solution: Implemented a change data capture (CDC) mechanism to track changes in the MySQL database and update Elasticsearch indices in real-time. Used AWS Lambda functions to process database change events and synchronize data between MySQL and Elasticsearch. This ensured that search results were always up-to-date and consistent with the database.

  5. Optimizing Code for Serverless Functions
     Challenge: Certain functionalities handled by serverless Lambda functions needed to be optimized for performance and cost-efficiency.
     Solution: Refactored the code for serverless functions to minimize cold start latency and optimize execution time. Used environment variables and AWS Secrets Manager to manage configuration and secrets securely. Implemented monitoring and logging using AWS CloudWatch to track performance and identify areas for improvement. Fine-tuned resource allocation (memory and timeout settings) to balance cost and performance.

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• Description
  Alpolink is a platform for selling exam dumps, developed using PHP and the CodeIgniter framework, with MySQL as the database management system.

• Role and Responsibilities
  As a senior software engineer for Alpolink, my responsibilities included addressing architectural challenges and optimizing system performance.

• Challenges Faced

  1. Architectural Complexity
     Challenge: Each website had its own frontend and database instance, leading to complexities in managing and maintaining multiple codebases and databases. This fragmented architecture resulted in higher operational overhead, difficulties in applying updates, and increased risk of inconsistencies.
     Solution: Implemented a unified backend architecture where a single backend serves multiple websites. This approach consolidated the codebases and databases, reducing complexity. The unified backend enabled centralized management, allowing for easier maintenance and scalability. Updates and bug fixes could be applied universally, improving operational efficiency and consistency across the platform.

  2. Performance Optimization
     Challenge: The platform needed to handle a growing number of users and data without compromising speed and reliability. The initial architecture with multiple database instances led to inefficient resource usage and slower response times.
     Solution: Optimized the system by consolidating the databases, which allowed for better indexing and query handling. Implemented caching mechanisms using Memcached to store frequently accessed data, reducing the load on the database and improving response times. Additionally, code optimization and load balancing techniques were applied to enhance overall system performance.

  3. Scalability
     Challenge: Ensuring the platform could scale efficiently to accommodate more websites and users without significant overhead in maintenance and resource allocation.
     Solution: The unified backend architecture inherently provided a scalable solution. By managing a single codebase and database system, adding new websites to the platform became straightforward. Employed containerization Docker to automate deployments and manage resources dynamically. This ensured the platform could handle increased load and scale horizontally as needed, all while maintaining ease of maintenance and operational simplicity.

  4. Data Consistency and Integrity
     Challenge: Ensuring data consistency and integrity across multiple websites and a single backend can be challenging, especially during high traffic or concurrent access scenarios.
     Solution: Implemented database transactions to ensure data consistency and integrity during multiple operations. Used MySQL’s ACID properties to maintain data accuracy and reliability. Applied data validation both at the application and database levels to prevent incorrect data entry.

  5. Deployment and CI/CD
     Challenge: Managing deployments and continuous integration/continuous deployment (CI/CD) for a platform serving multiple websites.
     Solution: Set up a robust CI/CD pipeline using tools like Jenkins to automate testing, building, and deployment processes. Containerized the application using Docker, enabling consistent environments across development, testing, and production.

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• Description
  Aanganpk is a multivendor ecommerce platform specializing in Pakistani women's handcrafted items. The platform is built using WordPress with WooCommerce plugin, leveraging PHP for customizations and extensions.

• Role and Responsibilities
  As a senior software engineer for Aanganpk, my responsibilities included addressing platform limitations and customizing functionalities to meet business requirements.

• Challenges Faced

  1. Platform Limitations
     Challenge: WordPress and WooCommerce, while flexible, have inherent limitations in handling complex multi-vendor functionalities, which can affect performance and scalability.
     Solution: Extended WooCommerce functionalities using custom PHP code to better handle multi-vendor operations. Developed custom plugins and utilized hooks and filters to tailor the platform to specific business needs without compromising performance. Optimized database queries and implemented caching mechanisms to enhance performance.

  2. Vendor Management
     Challenge: Managing multiple vendors with varying requirements and ensuring a smooth onboarding process was complex.
     Solution: Created a customized vendor dashboard using PHP and WooCommerce hooks, providing vendors with tools to manage their products, orders, and profile. Implemented role-based access controls to ensure vendors could only access their own data. Developed comprehensive documentation and an onboarding guide to facilitate a smooth vendor setup process.

  3. Customization and Extensibility
     Challenge: The need for custom features not available in standard WooCommerce and WordPress plugins to meet specific business requirements.
     Solution: Developed custom PHP plugins and extensions to add the required features. Used child themes and custom templates to modify the frontend appearance and functionality without affecting the core theme. Ensured all customizations adhered to WordPress coding standards for maintainability and compatibility with future updates.

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• Description
  The Information Retrieval System is designed to retrieve relevant documents from a corpora using machine learning techniques. It utilizes Support Vector Machine (SVM) algorithms for classification and is implemented in Python within Jupyter Notebook environment. Data is stored and managed in a Cassandra database.

• Role and Responsibilities
  As the sole architect and developer of the Information Retrieval System, I assumed complete ownership of all project aspects. My role involved the design and implementation of sophisticated machine learning algorithms tailored for document classification and retrieval. This project epitomizes my capacity to conceive, execute, and refine complex technical solutions independently.

• Challenges Faced

  1. Handling Large Datasets
     Challenge: Managing and processing large datasets efficiently to ensure timely document retrieval and accurate results.
     Solution: Utilized the distributed nature of the Cassandra database to manage large datasets effectively. Implemented data partitioning and replication strategies to ensure high availability and fault tolerance. Leveraged batch processing and parallel computing techniques in Python to handle data preprocessing and feature extraction, significantly reducing processing time.

  2. Algorithm Optimization
     Challenge: Implementing and optimizing sophisticated machine learning algorithms like TF-IDF for document classification and retrieval.
     Solution: Developed custom Python functions for TF-IDF calculation, ensuring they were optimized for performance. Applied dimensionality reduction techniques such as Singular Value Decomposition (SVD) to improve the efficiency and accuracy of the retrieval process. Regularly profiled and optimized the code to eliminate bottlenecks and improve overall performance.

  3. Precision and Recall Evaluation
     Challenge: Evaluating the effectiveness of the retrieval system in terms of precision and recall to ensure relevant documents are retrieved.
     Solution: Designed and implemented evaluation metrics to measure the precision and recall of the retrieval system. Conducted extensive testing using a validation dataset to fine-tune the algorithms and improve retrieval accuracy. Used confusion matrices and ROC curves to visualize and analyze the performance, making data-driven decisions for further optimization.

  4. Data Storage and Management
     Challenge: Efficiently storing and managing a large volume of documents and metadata in a Cassandra database.
     Solution: Designed a scalable schema in Cassandra tailored for efficient retrieval operations. Used Cassandra’s indexing and query capabilities to ensure fast and reliable access to documents. Implemented data consistency and integrity checks to maintain the quality of stored data.

  5. Scalability and Performance
     Challenge: Ensuring the system can scale to handle increasing volumes of data and user queries without degradation in performance.
     Solution: Leveraged the distributed architecture of Cassandra to scale horizontally, adding more nodes as needed to handle increased load. Implemented load balancing techniques to distribute user queries evenly across the system, preventing bottlenecks and ensuring consistent performance. Continuously monitored system performance and made necessary adjustments to maintain efficiency.

• Description
  Stealth Addresses is a cryptographic project focused on generating secure and private addresses for transactions. It utilizes the x25519 algorithm for shared secret generation and the ed25519 algorithm for signature generation and verification.

• Role and Responsibilities
  As the sole proprietor and developer of the Stealth Address Library project, I assumed full ownership and accountability throughout its lifecycle. Responsibilities encompassed every aspect, from conceptualization and design to implementation and refinement. This project epitomizes my capacity to initiate, execute, and deliver complex technical initiatives independently.

• Challenges Faced

  1. Integrating x25519 and ed25519 algorithms
     Challenge: Integrating the x25519 algorithm for shared secret generation with the ed25519 algorithm for signature generation and verification posed challenges due to their different purposes and implementations.
     Solution: Implemented an initial stealth address generation mechanism using the shared secret methodology of x25519, ensuring that the process of creating secure and private addresses was initiated correctly. Instead of relying on standard libraries for ed25519, developed a custom core implementation based on RFC8032 specifications. This approach allowed for seamless integration and ensured that the unique requirements of the project were met, maintaining high security standards.

  2. Ensuring compatibility between cryptographic algorithms
     Challenge: Ensuring that the x25519 and ed25519 algorithms worked together seamlessly, despite their differing cryptographic purposes and underlying mathematical principles, was complex.
     Solution: Utilized advanced mathematical and cryptographic principles to design a robust mechanism for generating stealth addresses. This involved a deep understanding of both algorithms and their interaction to ensure compatibility and security. Conducted iterative testing and validation to verify that the integrated algorithms worked together as intended. This process helped identify and resolve any compatibility issues, ensuring the reliability of the stealth address generation process.

  3. Developing a custom implementation of ed25519
     Challenge: Creating a custom implementation of the ed25519 algorithm required a comprehensive understanding of its specifications and intricate details, as well as ensuring it adhered to security standards.
     Solution: Followed the RFC8032 specifications meticulously to develop a custom core implementation of ed25519. This ensured that the algorithm was implemented correctly and securely, avoiding potential pitfalls associated with standard libraries.

• Description
  Products Pair is a system designed to predict the probability of items being sold together. The Apriori algorithm is utilized to calculate these probabilities based on transactional data.

• Role and Responsibilities
  As a senior software engineer for the Products Pair project, my responsibilities included designing and implementing the predictive algorithm and optimizing system performance.

• Challenges Faced

  1. High latency in retrieving probabilities from transactional database
     Challenge: Implementing the Apriori algorithm was successful, but retrieving probabilities of product pairs from the transactional database (MySQL) each time resulted in high latency. This negatively impacted system performance and user experience.
     Solution: Implemented a denormalized data store that periodically updates data from the transactional database. This data store contains pre-calculated probabilities of product pairs, allowing for faster retrieval. Scheduled batch processes to update the denormalized data store at regular intervals, ensuring that the data remains current while minimizing the impact on the transactional database.

  2. Ensuring data consistency between transactional and denormalized databases
     Challenge: Maintaining consistency between the transactional database and the denormalized data store was crucial to ensure accurate probability calculations and system reliability.
     Solution: Developed a robust synchronization mechanism to ensure that updates in the transactional database are reflected in the denormalized data store without significant delays. Implemented conflict resolution strategies to handle discrepancies between the two data stores, ensuring data integrity and consistency.

  3. Optimizing the Apriori algorithm for large datasets
     Challenge: The Apriori algorithm can be computationally intensive, especially when dealing with large transactional datasets, leading to performance issues.
     Solution: Optimized the Apriori algorithm by implementing efficient data structures and pruning strategies to reduce the search space and computational overhead. Utilized parallel processing techniques to distribute the computation across multiple cores or nodes, significantly reducing the time required to calculate product pair probabilities.

  4. Handling real-time updates and maintaining low latency
     Challenge: Ensuring that the system can handle real-time updates and maintain low latency for probability queries was essential for providing timely and accurate predictions.
     Solution: Implemented incremental update mechanisms that allow the system to update probabilities of product pairs in real-time based on new transactional data without requiring a complete recalculation. Utilized caching strategies to store frequently accessed probabilities in memory, reducing the need for repetitive database queries and further lowering latency.

• Description
  The Reporting System is designed to generate multitenant reports using data cubes and slices. It provides insights into various dimensions such as time, product, category, branch, and brand, catering to the diverse reporting needs of hundreds of clients. Reports can be saved and they're continuesly updated.

• Role and Responsibilities
  As a senior software engineer for the Reporting System project, my responsibilities included designing and implementing the reporting functionalities, ensuring scalability and efficiency.

• Challenges Faced

  1. Making reports generic for hundreds of clients
     Challenge: Ensuring that the reporting system can generate and handle reports for hundreds of clients with diverse requirements and data structures posed a significant challenge.
     Solution: Designed a multitenant architecture that isolates data and configurations for each client, allowing the system to generate tailored reports while maintaining efficiency. Implemented a flexible configuration management system that allows customization of report dimensions and filters based on client-specific requirements.

  2. Calculating dimensions from transactional data for each client
     Challenge: Calculating dimensions such as time, product, category, branch, and brand from transactional data for each client required significant computational resources and time.
     Solution: Developed a mechanism to pre-calculate dimensions for data cubes from transactional data. These dimensions are then stored in a cache (e.g., Memcached) for efficient retrieval during report generation. Utilized batch processing to periodically update and pre-calculate dimensions, ensuring that the data remains current and reduces the load during real-time report generation.

  3. Continuous updating and saving of reports
     Challenge: Reports needed to be continuously updated with the latest data and saved for future retrieval, which required efficient mechanisms to manage data consistency and report storage.
     Solution: Implemented incremental update mechanisms that allow the system to update reports with new data in real-time, ensuring that reports are always up-to-date without requiring full recalculations. Utilized efficient storage solutions to save reports, including leveraging database partitioning and archiving strategies to manage large volumes of report data.

  4. Handling complex data cubes and slices
     Challenge: Managing complex data cubes and slices for generating detailed and multidimensional reports added to the complexity of the system.
     Solution: Developed dynamic data cubes that can be configured and adjusted based on client requirements, allowing for flexible and detailed report generation. Implemented efficient data slicing techniques to handle various dimensions and filters, enabling the system to generate reports quickly and accurately based on user-defined criteria.

  5. Ensuring data security and privacy
     Challenge: Given the multitenant nature of the system, ensuring data security and privacy for each client's data was paramount.
     Solution: Implemented robust access control mechanisms to ensure that only authorized users can access and generate reports for their respective clients.

• Description
  KidSafe is an application designed to provide a safe environment for children to watch YouTube videos. It utilizes the YouTube API to curate a selection of kid-friendly content.

• Role and Responsibilities
  As a senior software engineer for the KidSafe project, my responsibilities included implementing features, integrating APIs, and ensuring child safety and usability.

• Challenges Faced

  1. Manual video selection by parents
     Challenge: Initially, the app was designed to allow parents to manually select videos for their children. This approach was cumbersome and time-consuming for parents, reducing the usability and efficiency of the app.
     Solution: Shifted from manual selection to an automated process by integrating functionality to include all videos from a specified YouTube channel. This significantly streamlined the user experience, making it easier for parents to provide a curated list of kid-friendly content.

  2. Fetching videos by YouTube channel name
     Challenge: Integrating functionality to automatically include all videos from a YouTube channel posed a challenge, as the YouTube API does not provide direct access to fetch videos by channel name.
     Solution: Developed a Python service using Beautiful Soup to scrape the web and fetch the YouTube channel ID using the channel name. This involved parsing the HTML of the YouTube channel page to extract the channel ID. Once the channel ID was obtained, implemented recursive calls to the YouTube API to fetch all videos associated with the channel. This ensured that the app could automatically and efficiently gather all relevant videos for inclusion in the KidSafe app.

  3. Ensuring child safety and content appropriateness
     Challenge: Ensuring that all videos included in the app were appropriate and safe for children was critical.
     Solution: Implemented additional content filtering mechanisms to verify the suitability of videos. This included checking video metadata, descriptions, and comments for any inappropriate content. Added parental control features allowing parents to review and approve the automatically fetched videos before making them accessible to children, providing an additional layer of safety.

  4. Handling API rate limits and data fetching efficiency
     Challenge: Efficiently fetching large numbers of videos while respecting YouTube API rate limits was a technical challenge.
     Solution: Implemented rate limit management techniques, such as batching requests and using exponential backoff strategies, to ensure compliance with YouTube API limits. Optimized the data fetching process by implementing pagination and caching strategies, reducing the number of API calls and improving the performance and responsiveness of the app.

  5. Maintaining a user-friendly interface
     Challenge: Ensuring that the app's interface remained user-friendly and intuitive despite the added complexity of automated content fetching.
     Solution: Focused on designing a clean and intuitive user interface that simplifies navigation and content discovery for both parents and children. Ensured seamless integration of the new automated features into the existing interface, providing clear instructions and feedback to users throughout the process.

• Description
  The Notifications Service is designed to deliver up to 1,000 notifications per minute efficiently. It utilizes Firebase Cloud Messaging (FCM) for message delivery, Cassandra for data storage, and supports features such as retry mechanisms and recurring/one-time notifications.

• Role and Responsibilities
  As a senior software engineer for the Notifications Service project, my responsibilities included architecting the system, optimizing performance, and ensuring reliable delivery of notifications.

• Challenges Faced

  1. Inefficient query performance due to data model design
     Challenge: The initial data model had the partition key set as the job_id and the sort key as the next notification trigger time. This design caused slower query performance because queries for the next jobs to be executed needed to scan across all partitions, especially when multiple partitions had the same next notification trigger time.
     Solution: Redesigned the data model by setting the next notification timestamp as the partition key and the job_id as the sort key. This change ensured that all notifications scheduled for the same time were stored within the same partition. This redesign improved query efficiency by localizing the data related to the same trigger time within a single partition, thus reducing the need to scan multiple partitions and significantly enhancing performance and reducing latency.

  2. Handling high throughput of notifications
     Challenge: Delivering up to 1,000 notifications per minute required a system capable of handling high throughput without performance degradation.
     Solution: Architected the system to utilize parallel processing and asynchronous operations, ensuring that notification delivery could scale horizontally as the load increased.

  3. Ensuring reliable delivery of notifications
     Challenge: Reliable delivery of notifications, including retry mechanisms for failed deliveries, was critical for the service's success.
     Solution: Developed robust retry mechanisms to handle transient failures in notification delivery. Implemented exponential backoff strategies to manage retries, ensuring that the system did not become overwhelmed by repeated immediate retries.

  4. Supporting recurring and one-time notifications
     Challenge: The system needed to support both recurring and one-time notifications, adding complexity to the scheduling and delivery logic.
     Solution: Designed a flexible scheduling system capable of managing both recurring and one-time notifications. Implemented mechanisms to track and manage recurring schedules, ensuring accurate and timely delivery of notifications. Optimized data storage and retrieval to handle the different requirements of recurring and one-time notifications, ensuring efficient processing regardless of the notification type.

  5. Scalability and data consistency
     Challenge: Ensuring that the system could scale to handle increasing load while maintaining data consistency was a critical challenge.
     Solution: Built the system on a scalable infrastructure, leveraging Cassandra's distributed nature to handle large volumes of data and high throughput. Implemented strategies for ensuring data consistency across distributed nodes, such as using lightweight transactions and carefully designed consistency levels in Cassandra.

  6. Latency and timely notification delivery
     Challenge: Minimizing latency and ensuring timely delivery of notifications were essential for the service's effectiveness.
     Solution: Optimized data access patterns to reduce latency in retrieving and processing notifications. The redesigned data model played a crucial role in achieving this by localizing relevant data. Implemented real-time processing techniques to ensure that notifications were delivered at the correct times, leveraging efficient scheduling and immediate processing upon trigger events.

Would you like me to add these directly into your Markdown as a block or reorganize them by category?

• Description
  The Google Map Scraper is a tool designed to extract information about stores from Google Maps based on location and store type. It is built using Python, with Beautiful Soup and Selenium utilized for web scraping.

• Role and Responsibilities
  As the sole creator and developer of the Google Map Scraper, I orchestrated all facets of the project, from conceptualization to implementation. My responsibilities encompassed designing the scraping process, integrating essential web scraping libraries, and fine-tuning data extraction mechanisms. This project underscores my adeptness in independently driving and delivering complex technical solutions.

• Challenges Faced

  1. Dynamic Rendering of Google Maps
     Challenge: Google Maps uses dynamic rendering techniques that load content asynchronously, making it difficult for traditional web scraping tools like Beautiful Soup to access the dynamically loaded data.
     Solution: Employed Selenium to automate a web browser to interact with the Google Maps webpage. Selenium is capable of handling JavaScript and waiting for the page to fully render before accessing the content. This approach allowed for capturing all dynamically loaded data. Implemented mechanisms in Selenium to wait for specific elements to load completely, ensuring that all relevant data was available before starting the extraction process.

  2. Extracting Detailed Metadata
     Challenge: Extracting detailed information such as store addresses, star ratings, phone numbers, and photos from the rendered Google Maps page required a methodical approach to handle the complex HTML structure.
     Solution: Used Selenium to navigate through the Google Maps interface and extract metadata related to each store. This included using XPath or CSS selectors to locate and retrieve the necessary data. After obtaining the metadata, utilized Beautiful Soup to parse the HTML and extract detailed information about each store, including addresses, ratings, phone numbers, and photos.

  3. Managing Data Extraction Efficiency
     Challenge: Efficiently managing the data extraction process to handle multiple stores and pages while maintaining performance and avoiding timeouts or errors was critical.
     Solution: Implemented pagination handling in Selenium to navigate through multiple pages of search results. This ensured that the scraper could extract data from all relevant pages. Used concurrency techniques to speed up the extraction process while implementing throttling to avoid overwhelming the Google Maps servers and to adhere to ethical scraping practices.

  4. Data Accuracy and Consistency
     Challenge: Ensuring the accuracy and consistency of the extracted data was crucial, as discrepancies in store information could impact the reliability of the scraper.
     Solution: Incorporated data validation checks to verify the accuracy of the extracted information. This included cross-referencing data with multiple sources or validating against known patterns. Implemented robust error handling and logging mechanisms to capture and address issues during the scraping process, allowing for accurate data extraction and easier debugging.

Here are some of the AI agents I have built to solve real-world challenges.

Built a Python multi-step agent that automatically discovers, extracts, normalizes, and packages Android system methods into an MCP-style JSON interface so AI agents can safely simulate calling Android functions (e.g., Bluetooth control, notification reads, media controls) in their training/testing environments. This eliminated manual reverse-engineering, improved simulation fidelity, and enabled RL/imitative agents to interact with Android-like services without device-level APIs.

+----------------------+    +---------------------+    +------------------+
|  User: capability    | -> | Service Discovery   | -> | Service Filtering |
|  (e.g., "bluetooth") |    | (search docs)       |    | (rank top N)      |
+----------------------+    +---------------------+    +--------+---------+
                                                               |
                                                               v
                                               +-------------------------------+
                                               |  Doc Fetch & Parse (HTML)     |
                                               +-------------------------------+
                                                               |
                                                               v
                                               +-------------------------------+
                                               |  Method Extraction            |
                                               |  (name, signature, args)      |
                                               +-------------------------------+
                                                               |
                                                               v
                                               +-------------------------------+
                                               |  Normalize to MCP-JSON        |
                                               +-------------------------------+
                                                               |
                                                               v
                                               +-------------------------------+
                                               |  Merge / Deduplicate          |
                                               +-------------------------------+
                                                               |
                                                               v
                                               +-------------------------------+
                                               |  Usability Filtering (rules)  |
                                               +-------------------------------+
                                                               |
                                                               v
                                               +-------------------------------+
                                               |  Validate & Generate Docs     |
                                               +-------------------------------+
                                                               |
                                                               v
                                               +-----------------+   +----------+
                                               | mcp.json (file) |   | docs.md  |
                                               +-----------------+   +----------+

• Problem Statement
  AI agents that learn to interact with external systems (Stripe, MongoDB, Zendesk, etc.) rely on documented APIs or Machine-Callable Proxies (MCPs). For Android system capabilities (Bluetooth, notifications, media controls, etc.) there is no single standardized MCP or simple callable API suitable for simulator training. Manually creating reliable simulated methods for Android services is time consuming and error prone. The goal: automate extraction and synthesis of Android service methods and produce a structured, MCP-like JSON that an AI training environment can consume.

• Solution (Overview)
  I designed and implemented a multi-step extraction agent in Python using a Chain-of-Responsibility pipeline. The agent:

  1. Identifies Android services related to a target capability (e.g., Bluetooth).
  2. Ranks and selects up to N (default 3) candidate services.
  3. Scrapes and parses official Android developer docs for each service.
  4. Extracts public methods, arguments, and descriptions.
  5. Normalizes methods to a shared JSON schema (MCP-like).
  6. De-duplicates and merge methods with identical signatures.
  7. Filters out lifecycle/internal methods that are not useful for simulation (e.g., onCreate, onDestroy).
  8. Outputs: list of source doc links, final MCP-style JSON, and human-readable documentation per method.

• Key Features

  • Language & Patterns: Python (modular), Chain of Responsibility design pattern for pipeline composability and easy step insertion/removal.
  • Robust parsing: HTML parsing with robust fallbacks (BeautifulSoup + regex + simple DOM heuristics) to handle doc variations.
  • Scoring & ranking: lightweight text relevance scoring to pick top candidate services.
  • Normalization schema: MCP-like JSON with fields: service_name, method_name, signature, args (typed), return_type, description, side_effects, required_permissions, source_doc.
  • Merging heuristics: signature equality, argument set similarity, and name synonyms mapping.
  • Usability filter: rule engine that marks lifecycle/internal/debug methods as non-callable for agents.
  • Extensibility: new steps can be plugged into the chain; step metadata (name, input_schema, output_schema) used for pipeline introspection and automated testing.
  • Outputs: machine-readable MCP JSON + readable Markdown docs for QA and developer review.
  • Safety: the pipeline flags potentially dangerous methods (e.g., factory_reset) to require manual review before simulation.

class Step:
    def __init__(self, name): ...
    def run(self, input): ...
# register
pipeline.add_step(ServiceDiscovery('discover'))
pipeline.add_step(DocFetch('fetch_docs'))
...

A research grade Python project that demonstrates how specialized LLM agents can collaborate to analyze complex medical reports. By running parallel, specialist agents (Cardiology, Psychology, Pulmonology) and synthesizing their outputs, the system produces concise differential assessments and recommended next steps - illustrating the potential of AI to augment multidisciplinary clinical reasoning. (For research & educational use only - not for clinical decision making.)

• The Challenge
  Medical reports are heterogeneous, dense, and often require input from multiple specialists to form a reliable assessment. Building an automated system that:

  • ingests diverse report formats,
  • reasons like multiple domain experts in parallel, and
  • synthesizes consistent, auditable conclusions was the core problem we set out to solve.

• The Solution
  We built a lightweight, production quality prototype composed of three parallel LLM-based specialist agents. Each agent focuses on a domain-relevant assessment, returns structured observations and recommendations, and then a synthesizer aggregates the results into ranked set of possible diagnoses with supporting reasoning. The architecture emphasizes modularity, reproducibility, and explainability.

• Key design principles

  • Specialization: each agent is optimized for one clinical domain to improve precision of reasoning.
  • Parallelism: agents run concurrently (threading) to reduce latency
  • Deterministic synthesis: outputs are combined and summarized with explicit reasoning chains so findings remain auditable.
  • Safety-first: project includes a clear non-clinical disclaimer and is designed for research/education, not patient care.

• Workflow

  1. Input - Synthetic or real medical report placed in Medical Reports/
  2. Parallel inference - Three GPT-5 powered agents (Cardiology, Psychology, Pulmonology) analyze the report in parallel threads and produce their observations, confidence scores, and recommendations.
  3. Aggregation - A combiner module consolidates outputs, resolves conflicts, and synthesizes a top-3 list of possible issues with concise rationale for each.
  4. Output - Results are written to Results/ (human-readable summaries plus structured JSON suitable for downstream evaluation.)
  5. Traceability - Each run includes agent prompts, model responses, andtimestamps for reproducibility and analysis.

• Architectural Choices
  Qwen 2.5 (32B): Achieved an excellent F1 score in cardiac diagnostic categorization tasks and demonstrated high computational efficiency.
  Gemma 2(27B): This model performed well in depression diagnosis tasks, suggesting strong capabilities for general mental health assessments.
  GLM-4.5(Vision-Language Model): This model excels at multimodal medical imaging analysis, which is crucial for evaluating conditions from chest X-rays or other visual data integral to pulmonology diagnostics.

This project involved the design and implementation of a sophisticated AI agent ecosystem engineered to autonomously manage and interact with a user's email inbox. The core objective was to shift email from a manual, reactive interface to a proactive, agent-driven workflow. The solution leverages a multi-agent architecture where, specialized LLM-powered agents collaborate to perform complex cognitive tasks traditionally handled by the user, including triage, summarization, information retrieval, and composition.

• The Problem
  Cognitive Overload in Email Management Traditional email interfaces present a homogeneous stream of messages, requiring significant manual effort for prioritization, comprehension of long threads, and information retrieval. This results in inefficient workflows, where critical signals are buried in noise, and valuable time is spent on repetitive organizational tasks rather than substantive work. The challenge was to build an autonomous system capable of understanding context, intent, and relative importance across a user's entire communications history.

• The Solution
  A Collaborative Multi-Agent System The solution is an AI-native platform built around a coordinated system of specialized agents. Each agent is designed with a specific capability and role, working within a shared context provided by a unified data layer. This architecture enables complex, multi-step reasoning about inbox state and user goals, moving far beyond simple prompt-and-response patterns to a true agentic workflow.

• System Architecture & Agentic Workflow
  The system is built on an event-driven, service-oriented backend that facilitates communication between discrete agents.

  1. Core Data & Context Layer

     • A real-time ingestion pipeline securely processes incoming emails.
     • All email content, metadata, and historical interactions are vectorized using text-embedding-3-small and indexed in a Pinecone vector database, creating a queryable "memory" of the inbox.
     • This semantic index provides the foundational context for all downstream agents, enabling them to reason across the entire corpus of communications.

  2. The Orchestrator & Agent Network
     A central Orchestrator agent, implemented using a state machine and rule-based router, analyzes system triggers (new email, user query, scheduled review) and delegates tasks to a network of specialized agents.

     • Triage & Classification Agent: This agent operates on every new email. It employs a fine-tuned transformer model (e.g., DistilBERT) to classify emails along multiple axes: priority level (critical, normal, low), required action (reply, review, archive), and contextual category (project-specific, managerial, external inquiry). The agent tags emails and can trigger alerts or route items to specific workflows.
     • Summarization & Synthesis Agent: Activated for complex threads or upon user request, this agent performs advanced comprehension tasks. Using a chain-of-thought prompting strategy, it first maps the thread's argument structure, identifies key contributors and decision points, and then reduces this to a concise narrative summary with explicit bullet points for actions, decisions, and open questions.
     • Autonomous Research Agent (RAG Engine): This agent powers conversational interaction. It handles natural language queries (e.g., "Summarize all feedback from client X in Q4"). The agent decomposes the query, performs multi-step semantic searches against the vector store to retrieve relevant information across disparate emails, and synthesizes the findings into a coherent, evidence-based report. It can cite source emails to support its conclusions.
     • Drafting & Communication Agent: Tasked with content generation, this agent composes replies, meeting summaries, or status updates. It is provided with the full thread context, the user's defined tone and style guide, and specific directives. The agent generates draft text that is contextually accurate and stylistically appropriate, which is then presented for review or sent autonomously based on confidence scoring.
     • Workflow Optimization Agent: This meta-agent monitors patterns in user interactions with the system (e.g., which auto-replies are edited, which triage labels are changed). It uses this feedback to fine-tune the prompts and decision thresholds of the other agents, creating a closed-loop system that adapts to individual user preferences over time.

  3. Execution & Integration Layer
     Agents publish their outputs and decisions to a common event stream. A real-time frontend subscribes to this stream, updating the UI dynamically. A Redis-based job queue manages long-running agent tasks, ensuring system responsiveness.

• Technical Implementation Details

  • Agent Framework: The agent logic was built using LangGraph, enabling the creation of cyclical, state-aware workflows that go beyond linear chains.
  • Models: A hybrid approach was used: GPT-4 for complex reasoning and synthesis tasks, GPT-3.5-Turbo for high-volume classification, and fine-tuned open-source models for specific, deterministic classification tasks to optimize cost and latency.
  • Memory: Implemented a hierarchical memory system. Pinecone provided long-term semantic memory, while Redis cached recent agent interactions and user preferences for short-term context.
  • Evaluation: Established a rigorous evaluation pipeline using PyTest and custom evaluator agents to score the output of other agents on dimensions like accuracy, relevance, and clarity against golden datasets.

Built a production-grade clause-generation pipeline (Python, Hugging Face, Llama/Falcon) that produces high-quality legal clauses under tight GPU constraints using quantization, cloud bursting, prompt engineering, and real-time token streaming to the frontend.

[Client UI] <--SSE/WebSocket--> [Stream Connector] <---> [Inference / Streamer]
                                           ^
                                           | (quantized or cloud full)
                                           v
                       [Quantization Module] <- [Model Router (Llama/Falcon)]
                                           ^
                                           |
                                     [Prompt Templating]

• Problem
  Legal clause generation requires contextually accurate, jurisdiction-aware text. Large LLMs produce high-quality clauses but often exceed local GPU memory; naive quantization can degrade quality; and interactive UIs need results streamed token-by-token so users don’t wait for full outputs.

• Solution
  Designed a modular pipeline that: selects Llama/Falcon checkpoints → applies quantization (4/8-bit) → fine-tunes on legal clauses → validates via semantic/syntactic metrics + human QA → serves inference with token/chunk streaming (SSE/WebSocket/gRPC). Heavy training runs use EC2 g5.2xlarge; local development uses quantized models.

• Workflow (Compact)

  1. Data prep: curate clause datasets with metadata.
  2. Prompt engineering: template library + A/B testing.
  3. Baseline eval: full-precision model quality baseline.
  4. Quantize + fine-tune: reduce memory, preserve quality.
  5. Inference + streaming: model streamer → stream connector → frontend.
  6. Evaluation: automated metrics (embedding similarity, BERTScore) + human legal QA.
  7. Deploy & monitor: APIs, logs, safety flags, reconnection/backpressure handling.

• Key Features & Tech

  • Tech: Python, Hugging Face, bitsandbytes-style quantization, Llama/Falcon, EC2 g5.2xlarge.
  • Quantization-aware tuning: 8/4-bit conversion + iterative fine-tuning to retain quality.
  • Prompt library: templated, constraint-driven prompts for legal accuracy.
  • Streaming: token/chunk streaming to frontend via SSE/WebSocket/gRPC with framed JSON {chunk,is_final,meta}.
  • Safety: incremental policy checks, rule filters, human review queue.
  • Modular architecture: pluggable backends and easy experimentation.

• Challenges & Solution (brief)

  • GPU memory limits: used EC2 g5.2xlarge for heavy runs; quantized models for development.
  • Quality loss after quantization: iterative fine-tuning + semantic/syntactic metrics + human QA.
  • Prompt effectiveness: systematic A/B prompt testing and template library.
  • Streaming robustness: implemented reconnection/resume, backpressure, and incremental safety checks.

An intelligent, conversational property assistant that combines Retrieval-Augmented Generation (RAG) with structured real-estate data to help buyers and renters find properties through natural language. Built for rapid demos and POCs, the app blends semantic search, context-aware dialogue, and multi-LLM support to deliver explainable, personalized property recommendations.

• The Problem
  Search interfaces for property discovery are rigid: users must translate natural preferences (e.g., near good schools, walkable, under $1,500) into filters and dropdowns. Traditional search struggles with fuzzy, multi-dimensional preferences and follow-up clarification across a conversation. The result: frustrated users and missed matches.

• The Solution - high level
  Implemented a RAG-based conversational assistant that:

  1. ingests tabular property data,
  2. retrieves candidate properties relevant to the user's intent, and
  3. synthesizes natural, explainable responses via LLMs while preserving multi-turn context.
     This architecture keeps the factual grounding of a database (preventing hallucination) while offering the natural interaction of an LLM.

• System Workflow (overview)

  1. Load and normalize property CSV (Pandas).
  2. Generate vector embeddings (FastEmbed) and load into vector store.
  3. user asks a question; the conversational agent extracts preferences.
  4. Agent performs a semantic retrieval of candidate properties.
  5. LLM synthesizes results into natural-language recommendations, with reasoning and match scores.
  6. Conversation memory preserves context across turns for follow-up clarification.

• Features (client-facing)

  • Natural language conversation (ask in plain English).
  • Context retention across multi-turn sessions.
  • Semantic property search and filtering (location, budget, amenities).
  • Multi-source support: local CSVs and URL-based datasets.
  • Flexible LLM integration: switch between OpenAI and open-source models.
  • Explainable recommendations: reasons and match percentages for each suggested property.
  • Rapid prototyping: Streamlit UI + run scripts

An intelligent, stateful travel assistant that combines LangGraph orchestration with multiple LLMs and tool integrations to plan trips, fetch live options, and prepare human-reviewed itineraries delivered by email. Designed for travel agencies and enterprise POCs, the agent blends conversational UX, safe human-in-the-loop controls, and observability for reliability.

• Business Problem
  Travel planning is multi-step, context rich, and frequently triggers costly mistakes (wrong dates, mismatched hotel class, visa constraints). agencies need conversational assistants that can (a) remember user preferences across a session, (b) reliably call external tools (flight / hotel search), and (c) hand over to humans for sensitive actions like booking or outbound emails - all while remaining auditable and easy to demo.

• The Solution
  We built a modular AI Travel Agent that uses LangGraph to orchestrate LLM reasoning and tool invocation. It routes tasks to the right model (fast models for parsing, stranger models for long-form email generation), fetches factual results from flight/hotel sources (SERP/third-party APIs), and surfaces human confirmation before any outbound action. The result: a conversational agent that feels like a travel consultant but remains controllable and observable for business use.

• How it works - concise workflow

  1. User input (Streamlit UI) - natural language request (e.g., Return flight Madrid -> Amsterdam, Oct 1-7, 4* hotel)
  2. Intent & slot extraction - lightweight LLM parses travel dates, locations, preferences. 3. Tool invocation - LangGraph executes search tools (Google Flights/Hotels via SERPAPI / scraping connectors) to retrieve candidate options.
  3. Candidate ranking & synthesis - LLM synthesizer ranks options, explains tradeoffs, and formats results (logos, links, short pros/cons).
  4. Human-in-the-loop review - user reviews, edits, or approves itinerary (agent pauses before email/booking).
  5. Email automation - upon approval, the system generates a polished HTML itinerary and optionally sends it via SendGrid.
  6. state & observability - conversation memory persists preferences; LangChain/LangGraph tracing records traces for debugging and analytics.

• Key Features (client-facing)

• Stateful conversations: remembers preferences across turns for seamless follow-up

• Human-in-the-loop safety: critical actions (booking, sending emails) require explicit user confirmation.

• Dynamic LLM routing: selects compact models for fast parsing and larger models for high-quality email copy.

• Email automation: generates and sends branded itineraries (SendGrid) after human approval.

• Observability: LangChain/LangGraph tracing hooks for debugging and performance telemetry.

• Steamlit demo UI: rapid, clickable prototype with visual results and links.

• The Problem
  A legal team at a startup spends countless hours reading and summarizing contract documents (NDAs, vendor agreements) to extract key clauses (dates, obligations, penalties). Manual review is slow and prone to oversight, especially when dealing with hundreds of pages of fine print. The client needed an AI tool to speed up contract analysis and ensure consistent coverage of important terms.

• Solution Approach
  We developed an AI-powered contract assistant using an open LLM and retrieval pipeline. The system automatically ingest PDF contracts (using OCR if needed), splits them into logical sections, and embeds each section into a vector store. A specialized LLM agent (e.g. a Claude of fine-tuned LLaMA model) is then tasked with summarizing sections and answering specific questions (e.g. "What is the termination notice period?"). By combining RAG with targeted prompts, the AI highlights obligations, flags risks, and produces a short bullet-summary of each contract. An optional fine-tuning or prompt-tuning step can adapt the LLM's style to legal terminology. This pipeline creates an automation workflow: ingest contract -> retrieve relevant sections -> LLM generates summaries/Q&A answers, which are then reviewed by lawyers.

• Architecture
  The system is a text-pipeline

[Contract PDFs] 
   ↓ (OCR/Text Extraction) 
[Document Chunks] 
   ↓ (Embedding Generator) 
[Vector DB (Pinecone/FAISS)] 
   ↓ (Retrieval) 
{Query/Question → Retrieve Relevant Chunks} 
   ↓ 
[LLM / Legal QA Agent] 
   ↓ 
Generated Summary / Q&A Answers

For example, the AI agent can run two passes: first, a "Clause Extractor" LLM identifies all key clauses (non-compete, indemnification) and stores them in a database; second, a "Summary Agent" LLM composes a plain-language digest and risk assessment. Vector search ensures only pertinent text is considered for each query. The entire architecture can be deployed on cloud VMs using HuggingFace inference APIs for LLaMA or via OpenAI's legal-pretrained models.

• Implementation Timeline
  • Week-1: Collect sample contracts and define required outputs (e.g. list of clauses, summary points). Build OCR/text pipeline and split documents into logical chunks. Generate embeddings and populate the vector store.
  • Week-2: Develop LLM prompts for summarization and Q&A. Prototype a simple question-answering loop: on sample queries, retrieve top clauses and prompt GPT-4/Claude to answer. Iterate prompts (few-shot examples) to improve precision on clause details. Week-3: Create user interface (e.g. web form or document uploader). Integrate agent so that legal staff can ask questions in plain language. Add monitoring and human-in-the-loop for review of low-confidence answers. Optimize performance to handle dozen of contracts/day.

EduGPT is an interactive AI Instructor that uses role-playing LLM agents to co-design a syllabus and then teach it - delivering personalized, adaptive learning experiences. Inspired by CAMEL and implemented with LangChain, EduGPT demonstrates a practical, extensible pattern for building agent-driven tutoring systems that scale across domains.

• The Problem
  Learners often struggle to get tailored, structured learning paths that match their goals and learning style. Building a good syllabus requires subject knowledge, pedagogy, and the ability to adapt pacing and explanations to the learner. EduGPT automates that process by having specialist agents negotiate a syllabus and then handing instruction over to an adaptive instructor agent.

• The Solution
  EduGPT orchestrates three cooperating agent roles:

  1. Two Role-Playing Agents (Syllabus Designers) - These agents discuss the user's learning goals, propose topics, and prioritize concepts through an interactive dialogue.
  2. Syllabus Generator - A synthesis step converts the agents' conversation history into a coherent, ordered syllabus (learning objectives, modules, and suggested exercises).
  3. Instructor Agent - The instructor teaches the user according to the generated syllabus, adapting explanations, pace, and examples to the user's feedback and progress. This pattern produces a dynamic learning plan and a personalized instructor without manual curriculum design.

• Workflow

  1. Agent initialization - Agents are primed with domain knowledge and pedagogical roles using LangChain prompts and role templates.
  2. User intent capture - The user provides learning goals and preferences (depth, duration, preferred format).
  3. Agent dialogue - Two role-playing agents run a guided conversation to enumerate and prioritize topics; conversation history is recorded.
  4. Syllabus generation - a synthesizer LLM reads the dialogue and emits a structured syllabus (modules, session goals, learning outcomes).
  5. Instructor handoff - The instructor agent receives the syllabus and begins an adaptive teaching session, taking user questions and adjusting content in real time.
  6. Session loop - The instructor tracks progress, adapts future lessons, and optionally logs interactions for continual improvement.

• Key Features

  • Collaborative syllabus design - two agents negotiate learning content, producing richer, multi-perspective curriculums.
  • Adaptive instruction - instructor modifies tone, depth, and pacing to match learner responses.
  • Interactive UI - quick demo via Gradio with real-time chat lesson flow.
  • Ligtweight deployment - designed for simple local demos or cloud POCs(Python 3.10+, LangChain).
  • Extensible architecture - add new subject agents, integrate quizzes, or plug in local LLMs.

An AI-first medical report analysis platform that turns uploaded PDFs into clear, personalized health insights using a resilient multi-model cascade. Built for clinicians and health-conscious users, it delivers fast, explainable, and secure analysis with session history and real-time feedback.

• The Challenge
  Healthcare documents (discharge summaries, lab reports, imaging notes) are dense, inconsistent, and hard for non-clinical users to parse. Clients wanted a solution that could:

  • Accurately extract structured data from diverse PDF reports (scanned or digital) up to 20MB.
  • Provide reliable, personalized insights while maintaining user privacy and secure sessions.
  • Improve over time by learning from past analyses and producing repeatable, auditable outputs suitable for clinicians and patients.

• Our Approach (Solution Overview)
  We built an intelligent agent-based system that combines robust PDF processing, a multi-model cascade for reliability, and in-context learning to continuously improve results. The system ingests a user's PDG, validates and extracts text, runs a cascade of LLMs (primary -> secondary -> tertiary -> fallback) to infer clinical findings, and returns actionable, personalized health insights in a clean, responsive Streamlit UI. All user activity is persisted securely in Supabase for history, traceability and model-context enrichment.

• Workflow

  1. PDF upload & validation - User uploads a PDF (<= 20MB). PDFPlumber extracts text and structured tables; files failing validation return clear, actionable errors.
  2. Preprocessing - Text normalization, OCR fallback for scanned pages, PII reduction options, and segmentation into clinical sections (e.g., Findings, impression, medications).
  3. Multi-model cascade (Groq orchestration)
     • Primary: meta-llama/llama-4-maverick-17b-128eistruct - first-pass high fidelity interpretation.
     • Secondary: llama-3.3-70b-versatile - deeper reasoning on ambiguous sections.
     • Tertiary: llama-3.1-8b-stntant - fast low cost checks and enrichment.
     • Fallback: llama3-70b-8192 - large-context recovery for very long or complex reports. Models are orchestrated to maximize accuracy, minimize latency, and reduce cost by routing only difficult cases to larger models.
  4. In-context learning & knowledge base - The agent uses prior session summaries and an internal KB to preserve context and improve consistency across repeated analyses.
  5. Result synthesis - Output includes: structured MCP-style JSON (for downstream systems), a clinician-friendly summary, patient-facing highlights, and recommended next steps.
  6. Session & history - All analyses are strode (Supabase) with versioning so users can revisit, compare, and audit past results.

MirrorGPT is a toolkit for building a private, personalized agent that mirrors a person - their facts, speaking style, tone, and optionally their voice. Designed for privacy-first personalization, MirrorGPT turns a user's documents, profiles and voice samples into a reusable Mirror Agent that can answer as you would, speak like you, and plig into apps for richer, person-centric experiences.

• The Problem
  Personalization at scale is hard. Off the shelf assistants can answer questions, but theyr rarely reflect an individual's preferences, values, or voice - and shipping that data to third-party services raises privacy and compliance concerns. Clients want agents that behave like subject-matter experts or trusted staff, but they also want control over the data and the ability to extend the agent into new context (chat, voice, app integration).

• The Solution overview
  MirrorGPT provides a pragmatic, privacy-first pipeline to create Mirror Agents

  • Local-first ETL to ingest personal data (LinkedIn PDF, resumes, blog posts, email snippets).
  • Transform stage to convert unstructured text into concrete, factual statements about the subject.
  • Load stage persist this distilled knowledge into vector-backed datastore for retrieval.
  • Mirror runtime that uses a base LLM prompted to consult the Mirror datastore when answering, producing responses aligned with the subject's facts and speaking style.
  • Optional voice cloning (ElevenLabs) to produce speech that mirrors that subject's voice. This approach balances fidelity (the agent speaks and reasons like the subject) with auditability and privacy (data remains in local stores and can be controlled by the owner).

• Concise Workflow

  1. Extract - Pull documents and media (URLs, PDFs, local files) and convert to text.
  2. Transform - Convert raw text into concrete statements and metadata (e.g., Gratuadet from X, Prefers concise summaries).
  3. Load - Index transformed statements into a searchable store(Chroma/DocArray) that is accessible to the Mirror runtime as a tool.
  4. Query/Interact - Run the MirrorAgent: prompts route the LLM to consult stored facts and emulate style; the agent returns text (and optional synthesized voice).
  5. Iterate - Add more data sources, tune prompts, and refine the Mirror's behavior (custom voice, persona rules).

• Key Features

  • Personalized knowledge base: Mirrors retain factual memory about education, experience, preferences and style.
  • Style & tone alignment: Responses are adapted to a target speaking style (concise, formal, friendly, etc.).
  • Optional voice cloning: Integrates ElevenLabs for voice personalization (voice ID management included).
  • Local-first privacy: personal data and mirrors can be stored locally (mirror/data/local) and kept out of public repos.
  • Extensible ETL: simple extract -> transform -> load scripts to absorb new data sources.
  • Pluggable datastore: supports Chroma/DocArray and can be extended to other vector stores.
  • Easy onboarding: step-by-step scripts to build a mirror from documents and start interactive sessions.

ShoppingGPT is an intelligent, retrieval-augmented shopping assistant that blends LLM conversation (Google Gemini), semantic routing, and fast product/policy retrieval to deliver accurate, context-aware product recommendations and shopping help. Designed for e-commerce demos and POCs, it routes user queries to specialized handlers (chitchat vs product search), grounds answers with data from a SQLite product store and FAISS policy vectors, and returns explainable results in friendly chat UI.

• The Problem
  E-commerce search and conversational assistants either rely on brittle keyword matching or on LLMs that hallucinate product facts. Customers want natural, guided shopping conversations that:

  • understand fuzzy preferences,
  • surface factual product details (price, availability, specs), and
  • explain why an item matches their needs - all while scaling and staying cost-effective.

• The Solution
  ShoppingGPT combines RAG and Semantic Router to get the best of both worlds: LLM fluency + database truth. The router classifies queries (chitchat vs product intent) and dispatches them to appropriate handlers. Product facts are retrieved from an indexed SQLite database (for precise details) while policies and guidelines are retrieved from FAISS vector store and used to ground and constrain LLM outputs. Gemini generates the conversational text, and the system synthesizes answers that are factual, contextual, and defensible.

• Architecture (concise)

  • Frontend: Flask web app (chat UI)
  • Semantic Router: GoogleGenerativeAIEmbeddings + Semantic Router Library - classifies and routes user input
  • Chitchat Handler: LLM-backed conversation flow with ConversationBufferMemory for natural dialog
  • Shopping Agent / Product Handler:
    • Product Search Tool: SQLite-backed product queries (case-insensitive, partial matching, indexed)
    • Policy Search Tool: YAISS vector store for company policies and returnable guidelines
  • LLM & Embeddings: Google Generative AI (Gemini-1.5-flash, Google embeddings)
  • Vector Store: FAISS (for policy documents)
  • Data ETL: ProductDataLoader class for efficient ingestion, indexing, and query formatting

• System Workflow

  1. User message arrives in the Flask chat UI.
  2. Semantic Router computes embeddings and uses a classifier (custom Hugging Face model hang1704/opendaisy) to decide intent.
  3. Dispatch:
     • If chitacht, route to the Chitchat Chain (fast conversational responses).
     • If product-related, invoke Product Search tool -> query SQLite -> retrieve candidate rows.
  4. RAG step: Retrieved product rows and policy snippets are passed to the Gemini LLM as grounded context.
  5. Synthesis: Gemini crafts the final chat message: product matches, ranked reasons, match scores, and actionable next steps (compare, add to cart link, policy note).
  6. Response is shown in the UI (and optionally logged for analytics).

• Key Features

• Natural conversation + grounded facts: Gemini conversational fluency combined with database truth from SQLite and FAISS.

• Semantic Router: accurate routing to specialized handlers - reduces hallucination and speeds responses.

• Advanced product search: case-insensitive partial matching and indexed queries for fast, relevant results.

• Policy-aware answers: consults FAISS-backed policy docs so responses comply with company rules (returns, warranties, promotions).

• Pluggable LLMs/embeddings: vendor-flexible - swap Gemini for alternative providers if desired.

• Problem Statement
  Employees at a mid-size company struggle to find answers in large collections of internal documents (handbooks, reports, FAQs). Searching dozens of PDFs and wikis is slow and error-prone, leading to lost productivity. A rapid solution was needed to allow staff (or customers) to ask natural-language questions and get accurate answers.

• Solution Approach
  We built a Retrieval-Augmented Generation chatbot using open-source LLMs and cloud APIs. First, documents are parsed and chunked each chunk is embedded (using an open source embedding model from HuggingFace) and stored in a vector database (Pinecone/FAISS). At query time, the user's question is embedded and a top-K similarity search retrieves relevant document snippets. These snippets, along with the query and chat history, form a prompt to an LLM (such as GPT-4 or a fine-tuned LLaMA2 model), which generates a concise, human-readable answer. The LLM can also be instructed via prompt-tuning to follow company style guidelines. This workflow automates knowledge retrieval and answer generation, blending AI agents (for search and generation) into a seamless Q&A service.

• Architecture
  The system uses a modular, scalable RAG pipeline. Key components are Document Ingest Pipeline, Vector DB, and LLM/Q&A Agent.
  Documents are pre-processed (OCR/PDF -> text, chunked) and indexed in a vector store. At runtime, a query's embedding retrieves matching chunks, which the LLM ingest to produce the answer. This text-based architecture can deployed on cloud servers or containerized with tools like LangChain or HuggingFace Transformers.

User Query
|
[Chat/CLI Interface]
|
[RAG Engine] -> [Vector DB]
^
|_ [Document Store / Knowledge Base]
	|_> (Relevant Chunks -> LLM prompt) -> [LLM Model] -> Answer

• Implementation Timeline
  • Week 1: Gather representative corpus and set up ingestion. Prototype embedding pipeline using OpenAI's 'text-embedding-ada-0021' or ar open model (Mistral-based) to generate and store vectors.
  • Week 2: Build RAG query flow. Implement API calls to the LLM (e.g. GPT-4) and integrate vector retrieval (using Pinecone/FAISS) to fetche relevant context. Develop prompt templates for the chatbot.
  • Week 3: Create a simple chat UI (or CLI) and refine prompts. Add few-shot examples or fine-tune for a domain accuracy. Iterate with sample queries, adjusting chunk sizes and retrieval parameters.
  • Week 4: Test end-to-end, implement feedback loops (e.g. storing failed queries for human review), and deploy on production infrastructure (cloud or on-prem).

• Problem Statement
  Drafting patent specifications, claims and abstract is time-consuming and requires specialized legal/technical language. Patent attorneys and agents will pay for tools that produce solid first drafts and save drafting hours.

• Solution Implemented
  A text agent that takes an inventor's plain-language description and structured inputs (diagrams, claim elements, priority data) and produces: detailed specification text, multiple claim variants (broad -> narrow), abstract, and a dependency map linking claims to spec paragraphs.

• Key Steps

  • Design prompts + few-shot examples for claim drafting and spec expansion.
  • Optionally fine-tune a model on public patent text (title/abstract/claims corpora) or use domain-adapted LLM from Hugging Face.
  • Provide template generation for claims: independent claim + dependent claims variations.
  • Embed prior art snippets and examiner-style objections into a vector DB (for referencing during drafting).
  • Integrate human review and version control; export to Word/PDF for filing.

• Architecture

[Inventor Input Form] 
   ↓ 
[Preprocessor → Structured Fields (elements, benefits, drawings refs)] 
   ↓ 
[Agent Orchestrator (LLM + Claim Templates + Prior Art Retrieval)] 
   ↓ 
[Draft Spec + Claim Sets + Abstract + Mapping] 
   ↓ 
[Attorney Review UI → Export / Filing Package]

• Problem Statement

• Compliance teams must review transaction logs and prepare Suspicious Activity Reports (SARs) and investigator summaries. This is laborious, highly regulated work - banks and fintechs will pay for accurate automation that reduces analyst road while keeping auditable trails.

• Solution Implemented
  Created a pipeline that ingests structured transaction records + associated communications, automatically extracts suspicious patterns, summarizes findings in investigator-ready format, and drafts SAR narratives that analyst can finalize.

• Key Steps

  • Stream transaction data pipeline (batch or near-real-time) into preprocessing.
  • Generate timeline narratives per customer by concatenating relevant events; embed narrative chunks and store in vector DB.
  • Use a specialized LLM agent to:
    • Classify risk categories (money laundering, transaction structuring, unusual geographies).
    • Generate an executive summary and a compliant SAR draft with templated fields.
    • Produce justification / evidence bullets linking to raw records (auditable).
  • Add confidence scoring and an approval workflow; log every AI suggestion for audit.

• Architecture

[Transaction Streams / Logs] 
   ↓ 
[Preprocessing & Event Aggregation] 
   ↓ 
[Timeline Builder → Chunking] → [Embedding Service] → [Vector DB] 
   ↓                                            ↑
[Detection Rules + Classifier] ──> [Agent Orchestrator (LLM)] 
   ↓ 
[Draft SAR + Evidence Links + Confidence] 
   ↓ 
[Analyst Review Dashboard + Audit Trail → Regulator Export]

• Problem Statement
  Responding to RFPs and producing tailored proposals consumes huge sales engineering time. Winning depends on speed and relevance. Enterprises and consulting firms will pay to get higher win rates and faster responses.

• The Solution
  An AI agent that ingests the RFP (PDF or plain text), pulls the company/requirements, maps them to a firm's capabilities library (pre-indexed), and auto-generates a draft proposal and a prioritized list of follow-up questions. It can also auto-populate boilerplate sections and estimate cost ranges.

• Key Steps

  • Index internal assets: case studies, capabilities description, pricing templates, bios.
  • Ingest RFP - extract requirements and scoring criteria (NLP extractor).
  • Retrieve top matching assets via vector search.
  • LLM agent composes: executive summary, scope of work, proposed timeline, risks, and task list.
  • Allow human editing; track modular responses to re-use in future RFPs (knowledge base growth).

• Architecture

[RFP Upload] 
   ↓ 
[RFP Parser (NLP extraction of requirements & criteria)] 
   ↓ 
[Retrieval: Vector DB of internal assets] 
   ↓ 
[Agent (LLM) → Draft Proposal + Questions + Cost Estimate] 
   ↓ 
[Proposal Editor (Frontend) + Versioning + Reuse Library]