Controlled POCs built on real engineering, documentation, and service workflows.
We combine AI expertise with hands-on machine, software, sales, and service experience to test solutions that fit the people, data, risks, and responsibilities around industrial equipment.
AI with industrial context
AI becomes useful when it is grounded in the way machines are engineered, sold, documented, commissioned, and serviced. That context helps us choose better use cases and set safer boundaries.
Technical documents, manuals, and source control
Service questions, faults, and expert knowledge
Engineering data, test evidence, and repetitive tasks
Ownership, security, operations, and fallback
From knowledge to controlled assistance
A useful AI POC has approved source material, known users, test questions, boundaries, and a clear way to stop. We build the smallest solution that can prove whether the idea deserves to become part of daily work.
Concrete examples
The best AI projects often start close to the work people already do every week.
Make manuals, technical documents, and internal knowledge searchable with an AI assistant.
A technician can ask questions about manuals, service reports, or project material and get answers based on the company's own documents.
See documentation assistant case →Create controlled image datasets for vision and inspection when real images are difficult or expensive to collect.
Generate variations of components with and without faults so an inspection model can be tested before cameras are mounted on the line.
See synthetic data case →Use AI to reduce manual work in service reporting, FAT notes, alarm history, and documentation.
AI can extract structured data from reports, combine status from multiple sources, or prepare consistent service notes.
Talk about automation →From idea to tool
Click a bubble to see what a practical AI case can look like for a technical company. The focus is small, concrete steps with low risk.
From example to test
A good AI test must be clear enough to assess value. That is why we start with a bounded data foundation, a visible before-and-after difference, and a short recommendation.
Collect an anonymized set of manuals, images, or support requests so everyone can see what material a POC actually starts with.
Make time spent, number of clicks, or information search visible before AI and after a prototype. That makes value easier to understand.
Show three steps: input, the AI agent's work, and finished output. That makes it easier for staff and leadership to assess whether the solution is usable.
Every POC should end with a short assessment: what worked, what must be adjusted, and what the next step costs in time and complexity.
Start with the concrete workflow: who performs it, how often, what errors cost, and whether data is available. If the answer points to repetition, accessible foundation, and measurable difference, it is worth testing in a bounded POC.
Typically existing documents, manuals, or data in a form we can work with. We clarify requirements, security, and realism first so you do not invest in something that will not hold up in operations.
Most bounded POCs take two to six weeks depending on data access, test scope, and number of users. We agree stop criteria and deliverables before start so the engagement has a clear end date.
Data is limited to what is necessary, access is role-controlled, and ownership of models and output is agreed in writing. The POC can run on-premise or in isolated cloud depending on your requirements.
Both are possible. Many industrial customers start on-premise or in an isolated environment. We choose architecture based on security, latency, and your IT policy, not a default cloud-first assumption.
Then you have a documented basis for stopping or adjusting scope. That is a valid and valuable outcome. The goal is to reduce uncertainty, not to push a solution forward.
See how the competencies connect in practice.
Bring one workflow, document set, or recurring service question. We will help scope a controlled POC with clear tests, boundaries, and stop criteria.
