From zero to production.Across industries.

We take on engagements at every stage — building products from scratch, scaling systems under pressure, and deploying AI into production environments that can't fail.

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Projects delivered

We take on engagements at every stage — greenfield builds, AI deployment, and legacy system modernisation..

Case study · AI report automation

AI that writes inspection reports with experts, and learns from them

Multi-agent AI
Human-in-the-loop
Report automation

Inspection reports are routine work for many companies, covering buildings, fire safety systems, electrical installations or machinery. Yet each report still depends on an expert reading through large amounts of data and writing it up by hand. That skill takes years to learn and is rarely written down. The result is slow work, reports that vary from expert to expert, and know-how that leaves with the people who have it.

We solved this by building a multi-agent AI system. One agent analyzes the inspection data. Another drafts the report using a knowledge base built together with the experts. An expert reviews and corrects every draft, and a learning agent turns those corrections into updates to the knowledge base.

As a result, reports are produced around 6 times faster, quality is consistent from report to report, the experts’ knowledge stays in the company, and the AI gets better every time it is used.

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Real problems, solved with AI that works alongside people.

Case study · AI bookkeeping automation

Document AI
Human-in-the-loop
Accounting automation

AI that automates the bookkeeping, so the accountant only reviews

Accounting is still one of the most manual and repetitive processes in a business. On both sales and purchases, people read numbers from invoices, type them in, choose the right accounts and post every entry by hand. The work is slow, mistakes are easy to make, and skilled accountants spend their time on data entry instead of advice.

We solved this by building an AI system that automates the bookkeeping from start to finish. It reads each invoice line by line, selects the correct cost account and prepares the posting. On sales, it suggests the right revenue account when a product is created, so no one has to look it up. An accountant approves or corrects every posting, and the AI learns from each correction.

As a result, accountants review instead of typing, bookkeeping is faster and more consistent, and control stays with the people responsible for it.

Case study · AI decision support

Multi-agent AI
Human-in-the-loop
Decision support

AI that helps people make the right decisions under pressure

When a serious incident happens, the people in charge have to make critical decisions fast. Information arrives from many sources at once, and they have to make sense of it under heavy pressure. It is hard to see what matters, easy to miss something important, and two people may handle the same situation differently.

We solved this by building a multi-agent AI system that supports the decision maker. Agents collect data from different sources, analyze it and filter out the noise, so only the information relevant to the decision is shown. The system suggests which actions to take and updates those suggestions as new information comes in. The decision maker stays in charge and makes the final call.

As a result, decision makers get a clear picture and a recommended next step, even when the situation changes by the minute.

Case study · Computer vision for quality testing

Computer vision
Mobile app
Quality testing

AI that reads test strips from a photo, so people don’t have to

Color test strips are a quick and common way to check quality, from water treatment to pools and laboratories. But reading them correctly is harder than it looks. Each strip combines several tests, and the user has to match every color to a reference scale by eye. The result depends on the lighting, on the person’s experience, and on whether they can tell colors apart at all, since some people are colorblind.

We solved this by building an app with computer vision AI. The user runs the test as usual and takes a photo of the strip. The AI analyzes the photo, compares each color to the reference scale and reads the values automatically. It works with an ordinary phone camera, indoors or outdoors. Every reading is saved together with its photo, so results can be checked, traced and shared later.

As a result, readings are fast, accurate and consistent, no matter who takes the test or where. No one needs training in reading colors to get a reliable result.

Case study · AI process improvement

Multi-agent AI
Human-in-the-loop
Process improvement

AI agents that find why processes break down, and what to fix

Every business has processes that are slower and costlier than they should be: orders that wait, cases that loop back, steps that get skipped. Finding out why usually takes weeks of workshops, interviews and manual analysis. Conclusions often rest on assumptions rather than data, and what is learned in one improvement project is rarely kept for the next.

We solved this by designing a multi-agent AI system that guides teams from finding to root cause. One agent spots issues in the process data, another turns them into clear problem statements, and a third identifies the root causes. A data analysis agent backs every conclusion with evidence, while the team refines each step through chat. Every decision and correction feeds a growing knowledge base.

As a result, root causes are grounded in data instead of guesswork, and the AI gets better with every improvement cycle.

Let's discuss how we can support your operations

If you are considering AI or software systems to support real business workflows, we are happy to discuss your goals and challenges.