I will build a production rag ai chatbot using vercel ai and vector dbs


About this gig
Generic AI wrappers confidently hallucinate in production. You need a High-Fidelity Retrieval-Augmented Generation (RAG) system that operates as a deterministic Truth Engine based strictly on your proprietary data.
I am an AI Architect building highly accurate pipelines utilizing Next.js, Vercel AI SDK, and Pinecone/Qdrant.
The Engineering Standard:
- Hybrid Search & Reranking: Combining semantic meaning with keyword matching, funneled through a Cross-Encoder to guarantee the LLM receives exact context.
- Tool Calling & Object Generation: Engineering the AI to output strict, machine-readable JSON or trigger external APIs deterministically.
- Human-in-the-Loop (HITL): Cryptographic UI approval gates for sensitive actions to ensure safety.
- Source Citations: UI tooltips linking directly to your source documents.
IMPORTANT: AI development requires exact scoping. Please CONTACT ME before ordering to discuss your data sources and set up a Milestone workflow.
Get to know Asad Javed
Fractional CTO and Lead Architect specializing in Nextjs SaaS JAMStack and AI
- FromUnited Kingdom
- Member sinceFeb 2021
- Avg. response time1 hour
- Last delivery1 year
Languages
English, German, French, Spanish
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FAQ
How do you prevent hallucinations?
I use Hybrid Search (vector + keyword) combined with a Reranking model, and I strictly prompt the LLM to only answer based on the retrieved context.
What is Human-in-the-Loop?
For destructive actions (like updating a database via tool calling), the AI pauses and asks the user to click an "Approve" button before executing the code.
Do I own the vector database?
Yes, following the Sovereign Code Doctrine, all accounts (Pinecone, Qdrant, OpenAI) are set up under your billing.

