We deploy AI where accounting offices, law firms and service companies read and retype documents. After launch we run the system and measure its results.
AI reads invoices, contracts and correspondence: data extraction, classification and automated document workflow.
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 everyday operation.
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.
Amounts are always computed by an engine or approved by a person.
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 the running system 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. Qkwit works on the same principle.
With a process audit. We sit with the people who do the work and map the process as it really is, with its documents, exceptions and manual workarounds. Only then do we pick a small scope for a pilot on real data, inside the systems you already use.
We agree the price with each company separately, for a specific scope: which process we automate and which systems it has to connect to. We learn the scope in the first conversation or in an audit, and that step comes with no commitment.
A pilot’s length depends on the process and the data: how many document types there are, how many exceptions, and the state of the data. We learn that in the audit, so we don’t give a timeline before then.
We process your data in the European Union. When the project ends, you get everything: the code and the 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.
Because we run it ourselves. All three of our products use AI every day: Qkwit reads and books documents, CheaperForDrug recognises a basket from a photo, Brokik adapts a lease to an instruction. We measure deployment results in the running system, not in a slide deck.
AI deployments
AI implementation ROI comes down to four numbers: hours of manual work, the cost of errors, cycle time, and the cost of maintenance. Skip a rigorous count of those four, and the result is a number on a slide, not a decision you can build a budget on.
AI deployments
An AI agent is not a chatbot with a longer memory. It is a system that carries out steps on its own: it reads a document, checks data in another system, prepares a decision. We build it under one rule: the agent proposes and code calculates and records, while a person approves the exceptions.
AI deployments
AI implementation that ends the day it goes live is really just getting started. Our own products have taught us a simple rule of thumb: building the system is about 40% of the work — keeping it running well is the other 60%.
