Many business AI projects stall in the same place: a brilliant prototype nobody opens on Monday morning. The model isn't the problem — where you put it is. We build custom AI agents inside the processes you already run — CRM, ERP, e-commerce, documents — pick only the use cases with a return you can calculate, and deliver a first prototype on your own data in two weeks.
The value doesn't come from adopting a model — it comes from choosing the right place to put it. A repetitive task with clear rules and data already available is the ideal candidate. A process that changes shape with every customer almost never is, and saying so up front saves months and budget.
We put systems into production that people actually keep using: assistants that handle recurring requests, search over internal documents that cites its source instead of making things up, product descriptions drafted by AI and then reviewed. Every use case starts from a measurement of the before, so the after can be verified — and no data ever reaches a model you haven't approved.
LangChain, AutoGen, CrewAI. Sequences that search, analyse, draft and hand over to a person when the case falls outside the expected.
Manuals, contracts, specifications, email: the system finds the right passage and cites its source instead of inventing a plausible answer.
OpenAI, Anthropic, Mistral or models hosted by you. The criteria are cost, latency and confidentiality constraints — not the fashion of the month.
Product descriptions generated and then reviewed, catalogue enrichment, a pre-sales assistant wired to real stock levels.
Lead qualification, first-line support, onboarding. Connected to the CRM, with a handover to a person the moment it's needed.
Demand and stock forecasting, anomaly detection, signals that arrive before the problem shows up in the accounts.
Half the value of this work is ruling out the wrong cases before spending months on them. These get discarded at the assessment stage.
Which data exists and in what state, which processes are candidates and what return is plausible for each. The output is a list ordered by value against effort.
Two weeks for a working version of the highest-impact case. It's measured on your data before committing to the full implementation.
Model choice, data flow, vector store, safety limits and the expected behaviour when the model doesn't know the answer.
Connection to CRM, ERP, e-commerce and databases. The system becomes a step in daily work, not a separate tool someone has to remember to open.
Tuning on real cases, testing on edge cases, structured collection of feedback to improve over time.
Gradual rollout alongside the manual process. Monitoring of accuracy, response times and cost per request.
A chatbot answers a question. An AI agent gets a task done: it looks up information in your systems, analyses it, drafts a reply or a document, and when a case falls outside what it expects, it hands over to a person instead of improvising. That last part — knowing when to stop — is what separates an agent you can run in production from a demo that impresses in a meeting.
| The fear | The answer |
|---|---|
| Spending months and budget for nothing | If the case doesn't stack up, we tell you before we start. It has happened three times. |
| An assistant that makes answers up | The system cites the source of the passage it uses instead of inventing a plausible answer. |
| Company data leaving the company | No data sent to models you haven't approved — including models hosted on your own infrastructure. |
A control tower for e-commerce with an agent that answers on the order data. It's a public demo: the agent answers from a script, because your data isn't behind it.
In 45 minutes we go through your processes together and pick out the ones worth automating — and the ones that aren't. The assessment is free, and it isn't a sales pitch.
A single, well-scoped use case — classifying incoming email, extracting data from documents, answering questions on internal manuals — runs between a few thousand and twenty or thirty thousand euros, plus metered model cost, which for normal business volumes is often under a few hundred euros a month. The figure that matters is a different one: how many person-hours a month that use case removes, and in how many months it pays back.
The data you are able to let out of your perimeter, and that list is decided before any code is written, not after. For confidential documents you use semantic search over your own content and pass the model only the fragment it needs, choosing a provider that does not train on data sent via API. Where even that is not enough, an open model hosted by you is the option: it costs more and performs less, but nothing leaves.
You do not let it answer from memory. The model answers only over documents retrieved at question time, cites which document a sentence came from, and says it does not know rather than improvising when it finds nothing. The rest is measurement: a set of real questions with expected answers, re-checked at every change to the system. An assistant without that set is not tested, it has merely been tried.
One where an error is cheap and the volume is high: routing, data extraction, a first draft of a reply that a person reviews. Avoid cases where a wrong answer reaches the customer with no human filter until the system has a measured track record. The practical rule is to start where AI saves time for the people doing the work, not where it speaks on your behalf.
Yes. No data is sent to models you haven't approved, and where confidentiality requires it we can use models hosted on your own infrastructure. The model is chosen on cost, latency and confidentiality, not on what's fashionable this month.