Real problems. Working systems.

The engagements below are described at capability level. We do not publish client names, logos, screenshots or performance figures, and we do not present a client's results as a promise to the next organization that asks.

Representative work

Three problems we have solved before.

Each describes the shape of the problem, what was built and the kind of value it created.

Public sector Computer vision

Secure multi-camera analytics

An operational platform bringing live video, face and vehicle intelligence, event search and controlled user access into a single governed environment, with every query attributable to a named user.

Business value
Faster investigation and clearer operational visibility
Document operations Automation

Documents and speech into structured data

Extraction and transcription workflows that organize incoming information, reduce manual entry and keep every output traceable back to its source so a person can check it quickly.

Business value
Less repetitive work and more consistent information
Enterprise analytics AI agents

Business questions answered in plain language

AI and voice-assisted data experiences that let decision-makers ask questions in their own words and receive governed, explainable answers drawn from approved sources.

Business value
Faster access to insight without technical friction

How we describe our work

Anonymous by default, and honest about it.

Plenty of consultancies publish logos and percentage improvements. We deliberately do not.

Several of our engagements involve public-sector operations, personal data or commercially sensitive processes. Naming those clients, or publishing figures precise enough to identify them, would be a poor way to demonstrate that we take confidentiality seriously.

It also keeps us honest. A performance number from one organization's process, with its own data quality and staffing, tells you very little about what would happen in yours. We would rather describe the problem accurately and then talk about your numbers.

In a conversation under a mutual NDA we can go considerably deeper — architecture, decisions, what went wrong and what we would do differently.

Where we work

Contexts we know how to operate in.

These describe the environments our delivery experience comes from, not a client list.

  • Public-sector operations

    Environments where access control, auditability and proportionality matter as much as the capability itself.

  • Document-heavy back offices

    Processes where information arrives as PDFs, scans, forms and recordings, and someone has to key it in.

  • Enterprise analytics teams

    Organizations with plenty of data and reporting, but a long queue between a business question and a trustworthy answer.

  • Companies exploring AI

    Teams with executive interest, a budget and no settled view yet on which problem to point it at.

Start a conversation

Recognize your problem in one of these?

We are happy to talk through how a comparable engagement was structured, what it cost in effort and where the difficulty actually sat.

experts@novatechai.site

See what we build