Summary
In 2024, we spearheaded the development of an AI-driven chatbot platform leveraging Retrieval-Augmented Generation (RAG) to deliver accurate, real-time semantic search, information retrieval and summarization from highly confidential documents. Designed for a high-stakes industry requiring utmost precision and security, this system integrated state-of-the-art LLMs with advanced backend and frontend technologies to meet stringent performance and security standards. The platform’s ability to cite its sources provided unparalleled transparency and trustworthiness, ensuring users could verify all generated responses against the original documents.
Technology Stack
- Backend: FastAPI
- Frontend: React & MUI
- Large Language Model (LLM): OpenAI GPT-4o
- Vector Database: Chroma
- Cloud Infrastructure: Google Cloud
- Database Management: Postgres (SQL)
- Containerization: Docker
- Authentication & Access Control: Auth0
System Architecture
Key Features/Deliverables
- Accurate Information Retrieval: Leveraged RAG techniques to efficiently retrieve and summarize information from thousands of documents with high precision.
- Source Citations: Provided inline citations that linked chatbot responses directly to source documents, ensuring response accuracy and traceability.
- High Security Standards: Implemented stringent data protection measures to safeguard confidential documents.
- User-Friendly Interface: Developed a clean, intuitive React-based frontend for seamless user interactions.
- Rapid Prototyping: Delivered an operational prototype under significant resource constraints, catering to an early-stage startup’s needs.
Outcome
- The system’s robust design and effective performance validated the early-stage concept, attracting a six-figure USD investment from venture capital firms.
- The prototype gained traction with several high-profile clients in the sector, who committed to multi-month trials, solidifying the project’s potential and viability in the market.
Contribution
We were responsible for the complete lifecycle of the project, from ideation and design to development and deployment. His contributions included:
- Architecting and developing the backend, frontend, and AI systems.
- Engineering secure data pipelines and scalable infrastructure.
- Coordinating with venture capital investors and stakeholders to refine the concept.
- Balancing technical execution with strategic decision-making under high-pressure startup conditions.