Custom
AI & data
Retrieval, assistants and the data work underneath them, built on your own documents and systems rather than on a general model's guess. For companies whose knowledge exists but cannot be found when it is needed.
The problem
The answer exists. It is in a contract, a spreadsheet, an old thread and three systems that do not talk, and finding it takes the person who has been here four years. Meanwhile the same question is asked, and answered differently, every week.
Why a package will not do
An assistant bolted onto a general model answers confidently and is wrong in the way that matters: on the specifics only your business knows. The gap is almost never the model. It is that nothing has been done to your own documents, records and systems to make them answerable.
That work is unglamorous and it is most of the project. Getting documents in and keeping them current. Deciding what a unit of knowledge is in this business. Retrieval that ranks by meaning rather than by keyword, hybrid where the question needs both. Evaluation, so an answer can be checked instead of trusted.
A product cannot do that for you, because it does not know your data. Ingestion pipelines, semantic search, RAG, and MCP where a model has to reach a live system rather than a snapshot of one — written against your sources rather than against a demonstration corpus.
And it has to stay a system rather than a demonstration. What people actually ask, which answers were wrong, what has to be re-indexed when a document changes: that is the difference between something still in use after a quarter and something that impressed a meeting.
How we approach it
01
Discovery
Where the knowledge lives today, who needs it, what an answer has to be right about, and where being wrong is expensive.
02
Architecture
Sources, ingestion, the retrieval strategy, and where a model may reach a live system rather than a copy of one.
03
Build
Built against your own corpus and evaluated on real questions, so quality is measured rather than demonstrated.
04
Scale
Re-indexing, new sources, and the questions people turn out to ask once they trust it enough to ask them.
Proof
LaTermen.ro, a platform built by our team: Romania's public registries ingested into one normalised corpus, a company linked automatically to its tenders, awards and court files, and assistants that answer with the issuing authority and article as fields on the result.
Is this the right fit?
For companies whose knowledge is real but unreachable — documents, records and systems that hold the answer and cannot produce it. If what you need is a chatbot answering from a handful of pages, an off-the-shelf product will do it more cheaply than we can, and we will say so.
It starts with a discovery, and that includes an honest look at whether your sources are in a state that can answer anything yet.
What a discovery includesContact