Assistants on your own data
Retrieval-augmented assistants that answer from your contracts, procedures, technical documentation and historical tickets — with citations back to the source paragraph, so answers can be verified rather than trusted.
Most AI pilots die between the demo and the department that has to use them every morning. We build the boring parts — data access, evaluation, fallbacks, audit logs — so the interesting part actually stays in production.
The models are a commodity — anyone can call the same API you can. What decides whether AI produces value in your company is everything around it: which data it can reach, how errors are caught, who reviews what, and whether anyone measured the result against the manual baseline.
Each of these has a clear before-and-after you can measure in hours saved or errors avoided. If a use case does not have that, we will tell you before you spend a budget on it.
Retrieval-augmented assistants that answer from your contracts, procedures, technical documentation and historical tickets — with citations back to the source paragraph, so answers can be verified rather than trusted.
Invoices, delivery notes, contracts, technical sheets and scanned forms turned into structured records in your ERP or database — including handwriting-heavy and badly photographed originals from the field.
Incoming tickets, emails, claims and applications sorted, prioritised and routed to the right team automatically, with the uncertain minority escalated to a person instead of guessed at.
Photo-based verification for installation, production and inspection work: detecting missing components, reading nameplates and serials, and flagging defects in images your crews already take.
Open-weight models running on STOREWEB infrastructure in the EU, so sensitive documents never leave hardware we operate. No per-token exposure of confidential material to third parties.
Data flow mapping, retention rules, decision logging, human-in-the-loop checkpoints and the technical documentation required where the EU AI Act applies to your use case.
We would rather prove one workflow works than sign a transformation programme. If the pilot does not clear the threshold, you stop with a small bill and a clear answer instead of a large bill and a maybe.
We pick one workflow with real volume, measure how it performs today and define the accuracy and time targets that would make automation worthwhile.
A working system on real data within weeks, scored against the evaluation set at every iteration, with the cost per processed item calculated openly.
Integration into the systems your teams already use, with review queues, alerting on quality drift and periodic re-evaluation as your documents and processes change.
That is a decision you make, not a side effect. We can run open-weight models entirely on STOREWEB infrastructure so no document ever leaves EU-hosted servers, or use a commercial API under a data processing agreement with retention disabled. Whichever route you choose, we document exactly where each piece of data travels.
A scoped pilot on a single workflow typically runs four to eight weeks: two weeks to define the task and assemble evaluation data, then iterations until the measured accuracy clears the threshold agreed in advance. We do not move a system into production before it beats the manual baseline on your own data.
Every system we deliver is designed around that assumption. Outputs carry confidence signals and source citations, low-confidence cases route to a human, and every decision is logged so errors can be traced and corrected. AI proposes; your process decides where a human must sign.
Usually less than expected. Most useful projects start from documents and records you already hold — contracts, invoices, tickets, procedures, historical decisions. The discovery phase establishes what exists, what is usable and what has to be cleaned before it is worth training or indexing anything.
It depends on the use case, and classification is part of our discovery work. Most business automation falls into low-risk categories with transparency obligations, while anything touching employment, credit or access to public services carries heavier documentation duties. We map your use case, and where obligations apply, the technical documentation is part of the deliverable.
Yes, and it usually produces the better result. Your team knows the processes and the data; we bring the AI engineering and the operational infrastructure. Repository access, architecture decisions and documentation are shared from the first sprint.
The best first conversation is not about AI at all — it is about the task that consumes hours every week and nobody enjoys. We respond within 48h with an honest read on whether AI is the right tool for it.