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From process analysis to running in production. We come into an organisation — an accounting office, a law firm, a services company — map the real processes and automate what pays off. We measure results in production, not in a slide deck.
what we deploy
AI reads invoices, contracts and correspondence: data extraction, classification and automated document workflow.
Chat and assistants that answer from the organisation’s real data and processes — not generalities from the internet.
AI takes routine decisions and escalates unusual cases to a human — with a full audit trail.
We connect AI to the systems the company already runs — from pilot to production operations.
engineering principle
AI
reads, classifies and proposes
deterministic code
computes money and critical decisions
We use this architecture in our own products — in Qkwit, taxes and social contributions are computed by a deterministic engine, not the model.
proof: our own products
All three of our products run AI (Claude) in production — every day: Qkwit reads and books documents, Taniej po Lek recognises baskets in Apteczkomat, and Brokik adapts lease documents and supports valuation. We don’t sell slide decks, we sell working deployments.
how the collaboration runs
We sit with the people who do the work and map the process as it really is.
Small scope, real documents and cases. We check whether automation pays off before it grows.
Integration with the systems the company runs on, escalation to a human and an audit trail.
We measure results in production and develop what brings a return. The same way we run our own products.
Example: in an accounting office, AI can read and classify client documents and prepare bookings for approval — while everything concerning taxes is computed by deterministic code. That is exactly how we built Qkwit.
questions
With a process audit. We sit with the people who do the work and map the process as it really is — with documents, exceptions and manual workarounds. Only then do we pick a small scope for a pilot on real data.
No. In our deployments AI reads, classifies and proposes, while everything concerning money and critical decisions is computed by deterministic code — the same data always gives the same result. That is how we built Qkwit, where taxes and social contributions are computed by the engine, not the model.
The case goes to a human. We design confidence thresholds and escalation from the start: low confidence means a question, not a guess. Every decision leaves an audit trail — who, when and on what basis.
No. We integrate AI with the systems the company already runs — from pilot to production operations. Replacing a system is a separate decision that an AI deployment doesn’t force.
Because we run it ourselves. All three of our products use AI (Claude) in production every day: Qkwit reads and books documents, Taniej po Lek recognises baskets, Brokik adapts lease documents. We measure deployment results in production, not in a slide deck.
from the blog
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first step
A few sentences about how the work looks today is enough. We’ll reply whether and how AI can realistically take the load off — and where we would start.
Tell us about your process