FyntAgent
Contextual AI Knowledge Retrieval
The Challenge
Standard ChatGPT interfaces fell short because they lacked deep organizational context. Employees spent hours searching through fragmented Google Docs, notion wikis, and PDFs.
We needed to engineer an AI agent that could instantly scan internal corporate repositories and answer complex questions via semantic search, strictly eliminating LLM hallucinations.
Tech Stack
- OpenAI
- Pinecone Vector DB
- LangChain
- Vercel AI SDK
- TypeScript
Capabilities
- RAG Architecture
- Vector Search Optimization
- Streaming API Design
The Engineering Solution
Semantic Data Embedding
Developed an ingestion pipeline that chunks thousands of raw markdown and PDF files, converting them via OpenAI's text-embedding models into a rigorous Pinecone vector database.
RAG Context Injection
When a user queries the bot, the system performs a semantic similarity search across the vector DB, grabs the relevant document chunks, and injects them directly into the system prompt to guarantee factual grounding.
The Impact
FyntAgent cut down internal documentation research time from hours to seconds, proving that RAG architectures, when engineered properly, yield massive enterprise ROI without the risk of hallucination.