AI/ML & generative AI

We integrate AI/ML: RAG over your private data, a copilot inside your workflows, classification and forecasting. Launched fast, without leaking your data.

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RAG + vector search

Your private data stays yours.

Copilot in your workflows

Autofill, suggestions, drafting reports.

ML models

Classification, forecasting, recommendations, fraud.

Case

In Defender-3, a messenger-based sales system, computer vision reads photos of the customer's documents and the system issues the invoice, contract, delivery note and waybill. The customer confirms what was recognised, so a recognition error never reaches a document.

An AI assistant works inside the admin panel: it answers the manager's questions about orders and data rather than replacing the manager.

We do not sell "AI". We embed a model into a process where it saves hands and leaves the decision to a person.

See the Defender-3 case study

Results

20–40% of knowledge-work time saved
Better decisions and real personalisation
Ready to scale

Why most AI projects never reach daily work

The demo impresses, the pilot works, and then it never takes root. The reason is usually not the model: there is nowhere to embed it. Data is scattered, the process is undocumented, the output lands nowhere.

Second, trust. If the system is sometimes wrong and the person cannot see what the answer rests on, they stop using it within a week.

What we do

AI inside the workflow

Not a chat box on the side but a step in an existing process: triaging a request, drafting a reply, classifying a document.

Document work

Extracting data from invoices, delivery notes and emails into accounting without retyping.

Computer vision

Video analytics, detection and tracking, PTZ guidance. Stream processing, not single frames.

Voice scenarios

Telephony and voice bots for routine calls, handing over to a person where a person is needed.

Verifiability

Every answer carries its source. Where there is no source, the system says so instead of inventing one.

Escalation to a human

The system recognises when a task is beyond it and passes it on rather than improvising.

How we approach it

We do not train our own large models and do not sell that as a service. We embed existing models into workflows so the result is checkable: structured output, mandatory sources, clear limits and escalation to a human. Less impressive than a demo, but it is what still works in month two.

Frequently asked

Do you train models for us?

Usually not, and usually it is unnecessary. Most tasks are solved by existing models plus well-prepared data and clear rules. Training is expensive and rarely justified.

What happens when the system is wrong?

It must show it, not hide it. We build so that every answer has a source and doubtful cases go to a person. A system without escalation is dangerous inside a workflow.

Will our data leave our systems?

That is a project decision taken at the start. Where data is sensitive, processing happens locally and only anonymised material goes out.

Plain explanations without marketing — useful even if you never hire us.

Custom system or an off-the-shelf box: an honest comparison

When a boxed product wins, when custom wins, and the third option: a working system adapted to your process. Lock-in, code ownership, support, migration risk.

SCADA, CRM and ERP: how they differ and what you need

Plain explanations of the difference between SCADA, CRM, ERP and WMS. What each one is for and why replacing one with another usually ends badly.

All answers