GPT in productionin your RAG, chatbots, and agents.

Internal chatbot on your documents (RAG)

A GPT chatbot connected to your document base rather than its own memory: procedures, contracts, catalogs, history. It cites its sources and flags when the answer isn’t in the corpus.

From ChatGPT to a real application

Many teams already use ChatGPT informally, with prompts copied from one person to another. We turn this usage into a shared application: access to the right data, permissions, history, traceability.

Agents & function calling

GPT calls your tools: CRM, ERP, billing, mailbox, internal API. The model decides which function to trigger, with which parameters, and the result feeds back into your information system.

Document and visual reading

Invoices, purchase orders, site photos, plans, screenshots: GPT reads, extracts useful fields, and returns them in a structured format, ready to write to your database. Output constrained by a schema.

Voice & real-time

Call transcription, automatic reports, voice assistants. This is an area where the OpenAI ecosystem excels, and often the deciding factor in choosing it.

Automations & workflows

GPT as a decision engine in n8n workflows: it sorts, writes, summarizes, triggers. AI becomes a process step rather than another tab left open by teams.

Source-backed answers, not free generation

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

The right GPT model at the right cost

High-end models validate feasibility; then we scale down until quality drops. An assistant running all day on a flagship model costs ten to thirty times more than a mid-tier one, often with no noticeable gain.

Framework before tool

Data sent via the API isn’t used to train models, unlike many consumer use cases. When localization is required, we use Azure OpenAI in a European region—and if that’s still not enough, we say so.

Measured, supervised, replaceable

Benchmark against your real cases before production, with quality and cost metrics tracked afterward. Model access remains isolated behind an abstraction layer: switching models doesn’t mean rewriting the app.

OpenAI’s managed API: latest models available on release day, the broadest ecosystem of tools and libraries, your data not used for training. The default starting point.

The same models served via Microsoft’s infrastructure, with a choice of European regions and integration with your existing Azure contract. New versions may lag by weeks.

When data cannot leave any US perimeter, GPT 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.