We build AI for concrete tasks

We always start with an evaluation. AI works best for narrow problems with clear input/output, sufficient data, and measurable success criteria. If your problem is too broad or data quality too low, we often recommend other solutions.

We validate early to catch that in time. If our prototype shows that AI does not outperform simpler methods, we say so and help you find a better solution instead.

It depends on the use case. For some classification problems, a few hundred examples are enough, while forecasting models often need more historical data. We help you evaluate if you have sufficient data.

We define clear, measurable criteria before we start building. This could be classification accuracy, reduction in manual work, or improved forecast precision. If we can't define measurable goals, you probably shouldn't invest in AI.

Yes, we design solutions that fit your tech stack. Integration is often the easier part. The harder part is making sure the problem is well defined and the data sufficient.

An initial evaluation and prototype typically takes 4-6 weeks. That gives us enough to validate if AI is the right tool. Full implementation varies by complexity, but we always recommend starting small.