Claude in productionin your RAG, chatbots, and agents.

Internal chatbot on your documents (RAG)

A chatbot connected to your document base: procedures, contracts, catalog, meeting notes. Claude answers by citing its sources and flags when the answer isn’t in the corpus, instead of making it up.

Support assistant & customer response

Claude drafts responses based on your ticket history and documentation, in your tone. Human review for sensitive cases, automatic sending for simple ones.

Tool-equipped agents (MCP, tool use)

Claude calls your tools: CRM, ERP, database, mailbox, internal API. The Model Context Protocol standardizes these connections—this is where Claude excels, and why we often choose it for our agents.

Long corpus analysis

Tenders, contract sets, audit reports, regulatory files. Claude’s very large context window allows processing an entire file at once rather than splitting it into chunks that lose the thread.

Document extraction & structuring

Invoices, resumes, purchase orders, meeting minutes: Claude reads, extracts the relevant fields, and returns them in a structured format, ready to be written to your ERP or database. Output is schema-constrained and validated before writing.

Code generation & review

Claude is our reference model for code: generation, review, migration, or legacy documentation. It’s also what we use to audit vibe-coded projects before taking them over.

Sourced answers, not free generation

We constrain Claude: RAG on your documents, explicit business rules, constrained output, human validation for critical cases. Every answer references its sources, and the model admits when it doesn’t know.

The right Claude model at the right cost

Opus for long reasoning and agents, Sonnet for most production cases, Haiku for classification and routing. We start high to validate feasibility, then scale down until quality drops.

Your data is not used for training

Data sent to the Anthropic API is not used to train models. When required, we use Bedrock or Vertex AI in a European region—or switch the use case to a sovereign model.

Measured, supervised, replaceable

Benchmarking on your real cases before production, with quality and cost metrics tracked afterward. Model access remains isolated behind an abstraction layer: changing models doesn’t mean rewriting the application.

Anthropic’s managed API: latest models available on release day, no infrastructure to manage, your data not used for training. This is the default starting point for most of our projects.

Claude is also available via Amazon Bedrock and Google Vertex AI, with a choice of European region. Useful when the cloud contract already exists, billing must go through this provider, or location is required.

When data cannot leave any US perimeter, Claude is not the right answer, and we say so. We then switch to Mistral or a self-hosted open-weight model, with the same RAG architecture.