Evaluating a live assistant
Persona-driven multi-turn conversations, played against the RAG in production.
Data & evaluation for production AI Your AI demos beautifully.
Your AI demos beautifully.
Nobody can tell if it answers correctly.
That's why it never reaches production. We build the evaluation that measures what your system actually does, then we fix what it reveals: almost always, your data.
By CNRS-trained AI experts with 10+ years of experience, the team behind the Synalinks open-source framework.
Team based in the European Union
Your data never leaves the European Union
The step almost everyone skips
Building AI has become easy. Proving it holds up in production, almost nobody does.
In two years we've worked with many companies. Every time, the same finding: the problem was neither the model nor the code. It was the absence of usable data, the absence of evaluation, and no clear path to ROI.
AI now lets anyone ship a prototype in days, and many conclude that expertise is no longer needed. But a prototype isn't a production system. For a system to survive contact with reality it needs a dataset that genuinely represents your business (your records, your documents, your teams' know-how) and an evaluation that measures its answers instead of watching them go by. That's the step almost every organization skips, very large ones included: systems get validated on vibes, on a handful of examples that work, and the problems surface in production, in front of customers.
We do build AI systems. That is exactly why we start with the data and the evaluation: they decide whether the system holds, and they are the advantage your competitors can't buy.
SilosDocumentsSpreadsheetsPeople's heads
Raw & scattered Cleaning & curation
- Locate
- Structure
- Enrich
- Verify
- Maintain
AI-ready asset
- Structured
- Documented
- Traceable
- Purposeful
Work
What we've already built.
Anonymized engagements, described by the need, what we built, and the asset the client kept.
Switching models without breaking anything
A regression suite that compares every candidate model, answer by answer.
Insider threat detection
A labelled dataset built from scratch, then the classification system trained on it.
What we do
Four commitments.
Measure before you believe
We build the evaluation of your system: test cases drawn from your own reality, explicit criteria, a result you can replay on every model change. Engineered, not improvised, and it is what turns "it seems to work" into proof you can defend to a regulator, a board, or a customer.
Turn your proprietary data into an AI-ready asset
An evaluation doesn't just tell you the system fails, it tells you where. We audit, clean, structure, and model the data it implicates, turning raw tables and documents into well-defined, documented, trustworthy assets. This is the moat off-the-shelf AI can't touch.
Capture the expertise, not just the data
Your edge isn't only in your tables. It's the judgment, rules, and know-how living in documents and people's heads. We capture that tacit expertise and turn it into structured, documented knowledge a model can reason over.
Your teams autonomous, not dependent
We coach your decision-makers on picking the right use cases, and train your technical team to run and extend what we build. The goal isn't that you call us back, it's that you no longer need to.
How it works
From evaluation to a lasting asset, in the right order.
- 1
Measure
The assessment evaluates your system and maps the data it runs on. In a week, fixed scope and fixed price, you know what it actually does and you leave with your AI Roadmap.
- 2
Fix what the evaluation reveals
It almost always points to the same place: data that is incomplete, inconsistent, or a business rule nobody ever wrote down. We turn that data and know-how into structured, documented, model-usable assets.
- 3
Deliver & maintain
You keep the assets, documented, and the evaluation becomes your regression test: on every new model, you know whether something degraded before your customers do.
Who we help
Built for companies whose edge is what they know.
If your advantage is specialized knowhow and proprietary data, generic AI will always underdeliver: it doesn't know your field or your data. We turn your knowledge into the AI advantage only you can have.
Teams with stalled pilots
A year in, nothing in production. We measure first, then fix the data foundation, so what you build on it actually survives production.
Domain-expert firms
Specialized services, industrial, professional. “Generic AI doesn't know our field.” We make it know yours.
Data-rich, regulated sectors
Finance, legal, health, industry. AI you can actually audit and defend.
Get started
Find out what your AI actually does.
A short assessment, fixed scope and fixed price. We evaluate your system and the data feeding it, and you leave with your AI Roadmap: the exact gap, and what to fix first. Yours to keep, whether or not you work with us next.