Generic LLMs like ChatGPT are powerful, but they don't know your business data. They hallucinate, make up facts, and fail to provide specific support.
To solve this, we don't just "wrap" an API. We build a Retrieval-Augmented Generation (RAG) pipeline.
The Architecture
Here is the stack we use at Saarza to build enterprise-grade agents:
- Orchestration: Vercel AI SDK (streaming responses)
- Database: PostgreSQL (user data) + Pinecone (vector embeddings)
- Model: GPT-4o or Claude 3.5 Sonnet (for reasoning)
- Embeddings: OpenAI
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Step 1: Chunking & Embedding
You cannot feed a 100-page PDF into a prompt. We split your data into semantic chunks (usually 512 tokens) and convert them into vector embeddings.
This allows the AI to search by meaning, not just keywords.
Step 2: The Retrieval Context
When a user asks, "How do I reset my API key?", we don't send that straight to GPT-4.
- We query Pinecone for the 3 most relevant documentation chunks.
- We inject those chunks into the System Prompt as "Context".
- We instruct the model: "Answer strictly using the provided context."
The Result
The result is an agent that speaks your brand voice, knows your latest pricing, and never makes up answers.
Ready to automate your support?