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AI

Generative AI integration for your product, from Paris

Copilots, semantic search, RAG over your documents, summaries and extraction, shipped inside your product with evals, guardrails and a cost budget.

In short

ZR Digital is a Paris AI and product engineering studio that integrates generative AI into existing and new products: in-app copilots, semantic search, retrieval-augmented generation (RAG) over your documents, summarisation and structured extraction. We choose the model per use case, including Mistral, EU-hosted or self-hosted options, and ship with evaluations, guardrails, and latency and cost budgets.

The best generative AI features do not feel like AI. They feel like the product finally understood what the user meant. ZR Digital integrates generative AI into your product from Paris: copilots inside your app, search that understands questions, answers grounded in your documents, and extraction that turns messy input into clean data.

We work on products that already have users, and on new ones built with AI from the first sprint. Either way the job is the same: find the places where a language model removes real friction, ship them properly, and keep them reliable once thousands of people use them in ways no one predicted.

Where generative AI earns its place

  • Copilots. Drafting, rewriting, filling a complex form from a sentence, suggesting the next step in a workflow. Scoped to your domain, aware of the user’s context and permissions.
  • Semantic search. Users type what they mean, not the exact keyword. Embeddings in PostgreSQL with pgvector, combined with classic filters so results stay precise.
  • RAG over your documents. Answers built from your contracts, manuals or knowledge base, with sources shown. Document-level permissions are enforced at retrieval, so the model never sees what the user may not.
  • Summarisation and extraction. Long threads, calls or reports turned into summaries; invoices, forms and emails turned into structured fields your system can trust after validation.

We also say no. If a feature would be better served by a good filter, a template or a clearer screen, that is what we recommend.

Choosing the model

Model choice is an engineering decision, not a brand preference. We score candidates on the same evaluation set built from your data, then weigh quality against latency, cost per request and where the data is processed.

  • Frontier APIs from Anthropic (Claude) or OpenAI when the task needs strong reasoning or writing.
  • Mistral when you want a European provider, with EU hosting available.
  • Open-weight models such as Llama or Mistral, self-hosted in your own cloud account, for high volume or strict confidentiality.

The integration stays portable. Swapping models later is a configuration change and an eval run, not a rewrite.

Making it reliable

Language models are probabilistic. The same question can produce a slightly different answer, and sometimes a wrong one stated with confidence. Retrieval can miss the right passage. Users will paste content designed to manipulate the model. We plan for each of these.

  • Evals run in CI on every change: retrieval quality, answer accuracy, format compliance, refusal behaviour.
  • Guardrails on input and output, validation of structured responses, and defences against prompt injection following the OWASP Top 10 for LLM applications.
  • Privacy by design. Minimal data sent to the model, personal data masked where possible, retention and residency documented for GDPR, and transparency to users where the EU AI Act requires it.
  • Latency and cost budgets. Streaming responses, caching, smaller models for simple steps, and a dashboard that shows spend per feature.

Who builds it

ZR Digital is an AI and product engineering studio led by a founder-CTO with more than fifteen years of building software. We design, build and operate our own SaaS, BookCab.tech, for independent VTC drivers in France, so we know what it takes to keep a feature working after the launch announcement: monitoring, support tickets, and the discipline to remove what users do not use.

How we run it

  1. 01

    Frame the use case

    Who uses it, what a good answer looks like, what a bad one costs. We write the acceptance criteria before touching a model.

  2. 02

    Prototype on real data

    A thin working slice on your own documents, so everyone judges real output instead of a demo.

  3. 03

    Choose the model

    Several models scored on the same eval set, then picked on quality, latency, cost and where the data is processed.

  4. 04

    Harden and ship

    Guardrails, access control, fallbacks, streaming UI and rate limits. Released behind a flag to a first group of users.

  5. 05

    Measure and improve

    Production traces feed the eval set. Prompts, retrieval and model choice evolve against numbers, not anecdotes.

Frequently asked questions

What is RAG and when do we need it?

Retrieval-augmented generation means the model answers from passages retrieved from your own documents rather than from memory alone. You need it when answers must reflect your content — contracts, product docs, policies — and cite where they come from. It reduces hallucination but does not remove it, which is why we evaluate retrieval and answers separately.

Will our data be used to train the model?

Not with the enterprise API terms of the major providers we use, which exclude training on your inputs by default. For stricter needs we use EU-hosted endpoints or an open-weight model running in your own cloud account. We document where every piece of data goes before launch.

Which model should we use?

There is no single best model. Frontier models from Anthropic or OpenAI often lead on complex reasoning; Mistral offers strong models with an EU hosting option; open-weight models make sense for high volume or strict confidentiality. We decide with an eval run on your data, and keep the integration portable so you can switch.

How do you handle wrong answers?

We design the interface so users can check: sources shown, uncertainty stated, easy correction. Behind it, guardrails filter unsafe output, structured outputs are validated, and an eval suite catches regressions before release. Some use cases keep a human review step permanently.

An AI use case in mind? A product to launch?

Describe the situation in a few lines. Within two business days you get a straight answer: what we would do, in which order, and whether we are the right team for it.

Talk about your project Or write to us directly