Integrating Claude into your business application

By Cédric Barme · Founder of NSY

Anthropic's Claude models are among the most capable on the market for enterprise use: document analysis, tool-using agents, controlled generation. But between the demo and the production application lies a craft — integrating an LLM with the same rigour as a critical component. That is the NSY approach.

The use cases that work

• Document assistant (RAG) — answering from your content: internal documentation, knowledge base, contracts. Documents are indexed as vectors, retrieved by similarity, and Claude synthesises with source citations.
• Tool-using agents — Claude that acts: querying your database, preparing a quote, filling a form — with strict guardrails on what the agent can and cannot do.
• Controlled generation — summaries, letters, structured content produced in your format, your tone and your constraints, with human review where the stakes demand it.
• Flow analysis — classification and extraction over volumes (inbound email, claims, documents) that nobody can read exhaustively.

What a serious integration handles — and the demo ignores

Confidentiality: which data goes to the API, which never does, upstream anonymisation where needed — non-negotiable questions as soon as GDPR or a regulated sector is involved. Costs: sizing the model per task (not everything deserves the most powerful model), caps, caching. Robustness: API error and outage handling, graceful fallbacks, retry queues. Evaluation: measuring answer quality before and after every change, rather than trusting impressions.

Why Claude — and when something else

NSY integrates Claude for its documented strengths: extended context windows, reliable instruction following, quality in French. But technical honesty comes first: depending on sovereignty requirements, budget or the nature of the task, Mistral AI or OpenAI may be the better choice — sometimes even a rule-based engine with no LLM at all, like this site's own chatbot. The recommendation follows your case, not a partnership.

How NSY works

A short scoping phase identifies the highest-yield use case, its data and its risks. A rapid prototype then validates feasibility on your real content, before industrialisation: integration into your application or into an AI-powered website, monitoring, skills handover. A software engineer since 2012, Cédric Barme applies critical-systems standards to LLM integrations — see also the FAQ.