Document & data processing
Invoices, waybills, contracts and forms read automatically and turned into structured records in your system — with the uncertain ones flagged for a human instead of silently guessed.
Applied AI
AI-Powered Process Optimization
We don't sell you a chatbot. We take the workflows your team already runs — the ones eating hours every week — and rebuild them so software does the repetitive part and your people do the judgement part.
The real cost
The bottleneck is rarely the software you bought
It's the manual work living in the gaps between systems — the copy-paste, the re-typing, the checking. That work is invisible on the balance sheet and expensive in practice.
- Hours spent re-keying data Invoices, orders, forms and contracts moved from one screen to another by hand.
- Errors found too late A wrong price, a duplicate record, a missed exception — caught after it cost something.
- Requests waiting in a queue Support and internal requests sorted by hand before anyone can act on them.
- Decisions made on gut feel The data to forecast demand or spot a trend exists — it just isn't usable in time.
What we build
AI where it actually pays off
Each of these is a working system that plugs into the tools you already run — ERP, CRM, e-commerce, or your own software.
Demand & stock forecasting
Your sales history, seasonality and campaign calendar turned into forecasts your purchasing team can act on — so you neither run out nor sit on dead stock.
Support & request triage
Incoming requests classified, routed to the right team and answered with a drafted reply — your agents edit and send instead of starting from a blank page.
Anomaly & error detection
Pricing mistakes, duplicate records, unusual orders and process deviations caught as they happen — not in next month's reconciliation.
Ask your own data
A question in plain language, an answer from your own records — with the source rows shown, so the number can be trusted and audited.
AI inside your existing software
You don't have to replace what works. We integrate AI into the ERP, CRM or custom system you already depend on.
How we work
A pilot before a promise
We measure the process before we touch it, so the improvement is a number you can check — not a claim you have to believe.
Process discovery
We sit with the team doing the work and map where the hours actually go. Often the biggest win isn't where you expected.
Week 1Data & feasibility audit
We check whether the data needed actually exists and is clean enough. If a use case won't work, you hear it here — not after the invoice.
Week 1–2Scoped pilot
One process, one measurable target, running on your real data. Small enough to fail cheaply, real enough to prove the case.
2–4 weeksProduction & measurement
We ship it into daily use, train the team, and keep watching the numbers. If the gain fades, we tune it.
Ongoing
Our stance
What we will and won't do
We tell you when AI is the wrong tool
Plenty of processes are fixed better by a rule, a integration or a cleaned-up form. If that's your case, we'll say so and build that instead.
A human stays in the loop
For anything with a cost of being wrong, the model proposes and a person approves. Confidence is shown, not hidden.
Your data stays yours
We agree up front where data lives and what may leave your systems. Where the requirement demands it, the model runs on your own infrastructure.
Software company first
We've been building and running enterprise software for years. AI is a capability we add to that — not a demo detached from production.
FAQ
The questions we get asked first
Do we need a data scientist or a big dataset?
No. Most of the value in process work comes from documents, records and logs you already have. The feasibility audit in week one tells you whether your data is enough — before you commit to anything.
Do we have to replace our current system?
No. We integrate into the ERP, CRM, e-commerce platform or custom software you already run. Replacing a working system is expensive and rarely the actual problem.
How long until we see something real?
The pilot runs on your real data within 2–4 weeks of the discovery session. That's a working process with a measured before-and-after, not a slide deck.
Will our data be sent to a third party?
Only if you agree to it, and it's written down before we start. Where regulation or policy requires it, the model runs entirely on your own infrastructure.
What if the AI gets it wrong?
It will, sometimes — that's why we design for it. Anything with a real cost of error goes through human approval, uncertain cases are flagged rather than guessed, and every decision is logged so you can audit it.
Next step
Bring us the process that annoys you most
One call. We'll tell you honestly whether AI helps here, what it would take, and what it wouldn't fix.