LangChain to orchestrate AI applications in production

We start with a fast audit of product context, codebase and business priorities.

Then we run a shared backlog with short iterations and frequent deliveries.

Yes. We audit the current stack, fix critical issues and restore delivery flow.

The objective is to stabilize quickly without freezing your roadmap.

Yes. We act as an extension of your team with transparent technical steering.

Decisions, backlog and rituals are shared end to end.

When the use case goes beyond a simple GPT call: need for conversational memory, external tool use, multi-step reasoning, or search across your data.

For simple cases, a direct OpenAI API call is often enough. We help you decide without over-engineering.

Quality of vector indexing, adapted chunking strategies, result reranking, mandatory citations in responses, continuous evaluation with LangSmith.

A reliable RAG is built iteratively, measuring relevance on real cases.

LangSmith traces every chain execution: prompts, tool calls, latency, costs, response quality. We couple it with our own business metrics.

Without serious observability, a LangChain application quickly becomes a black box that is impossible to debug.