Artificial intelligence
Artificial Intelligence
Putting a model inside the business process — for daily use, not for a demo.
- ID
- AI-01
- Rollout stages
- 05
The business problem
The decision about AI is usually framed as a technology choice, when the real question is different: which operation is done by hand today, and where is its result measured. That is exactly why pilots stall — the model works but is not wired into any process, and two months later nobody uses it.
What we build
We start from a specific operation: checking a document, classifying an order, routing a request, preparing a report. A model is chosen for it, evaluated against your data, and the result is written back into the system you already use — CRM, ERP or an internal portal. A human approval step for the cases the model gets wrong is part of the architecture, not an afterthought.
What the client gets
- A working solution for one operation, with a measurable baseline
- Model accuracy evaluated against your own data, not a public benchmark
- A human review and correction path for the uncertain cases
- Results written automatically into the system you already run
Possible integrations
- REST / GraphQL API
- PostgreSQL
- Microsoft 365
- Google Workspace
- Bitrix24
- 1C
Rollout stages
- 01
Audit and analysis
We measure the process, check the quality of the available data and record the baseline.
- 02
Architecture and design
We compare model options against your own examples and agree the accuracy target.
- 03
Development
We build the processing pipeline, the review interface and the results log.
- 04
Pilot operation
We run it on a limited slice of the real workload and compare accuracy against the baseline.
- 05
Full rollout
We move to full volume and set up monitoring plus alerts for quality drift.
Security approach
What the model can see is restricted at the request level. Sensitive fields are masked before processing, and the query history stays open to your audit. If a cloud model is used, exactly what goes to which provider is documented.
Ongoing support
Model accuracy drifts: the data changes, the process changes. Support therefore covers quality monitoring, periodic re-evaluation and updating the model when it is needed.
Related services
- AI-02
Private & Local AI
The model runs on your server; documents never leave the company network.
- AI-03
AI Agents
A system that acts rather than answers: it checks, writes, hands over and records the outcome.
- DI-02
Data Analytics & Business Intelligence
One number, one source: reports stop being something people argue about.
Consultation on this practice
Describe your situation briefly — we will outline the options and the first step.