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AI Integration

LangChain and RAG AI Integration Sprint.

Add production AI to an existing product with LangChain, LangGraph, RAG, agents, streaming chat, monitoring, evaluation, and cost controls.

Send project context
01_Search Intent Covered
Add RAG to a product
Build an AI support agent
Integrate LangChain or LangGraph
02_What Gets Fixed
Product knowledge is connected through grounded retrieval instead of generic model answers.
Agent workflows are instrumented with traces, evaluations, and cost controls.
The AI feature ships as a product surface with safe fallbacks and human handoff paths.
LLM chat interface, streaming responses, context management, and prompt architecture.
RAG with pgvector or equivalent retrieval so answers stay grounded in product knowledge.
Voice workflows with LiveKit, Deepgram, ElevenLabs, or the right provider for the use case.
Usage monitoring, model selection, prompt caching, evaluation checks, and cost controls.
03_FAQ

Can you build a RAG chatbot for my business?

Yes. I design the content pipeline, retrieval layer, prompts, citations, evaluation set, and production monitoring around your business knowledge.

Do you use LangChain and LangGraph?

Yes. I use LangChain components for model and tool integration, and LangGraph-style orchestration when workflows are stateful or need human review.

How do you prevent hallucinations?

I bound the agent to approved sources, require citations, add refusal behavior, evaluate against real examples, and monitor traces in production.

04_Schedule This Sprint

Pick a discovery slot and share the product link, repo context, or launch blocker. I'll confirm whether this sprint is the right fit.

05_Related Field Notes

When RAG Is Worth the Complexity for SMB Teams

A practical decision framework for RAG: data readiness, retrieval quality, evaluation, cost, ownership, and when a simpler workflow wins.

Building AI Support Agents Operators Actually Trust

A production playbook for support agents with bounded scope, retrieval, tool permissions, escalation rules, tracing, and evaluations.

Building Internal Knowledge Bases for Operations Teams

How to design an internal knowledge base for operational work: source ownership, chunking, retrieval, permissions, feedback, and evaluation.