The challenge
You can't commit to a full AI build on a hunch - but from the outside, you can't tell whether your data and your use case will actually deliver. The Sprint answers that before the big spend.
A big bet, made blind
Going straight to a build means committing serious budget before anyone has proven the data supports the use case. If it doesn't pan out, the cost lands after the money is already gone.
Is the data even good enough?
Most teams can't answer this until someone digs in. Sampling, gaps, whether the signal you need is actually present - these decide feasibility, and they stay invisible until you look.
Even if it works, how does it ship?
A model in a notebook isn't a product. Edge versus cloud, integration with your systems, where people stay in the loop - without a plan, a promising result just stalls.
How we solve it
A focused, ~4-week sprint run alongside your team. We use your real data from day one (CRISP-DM + MLOps), so every answer is evidence, not opinion - and your people leave more capable than they started.
Use case & solution definition
We frame the problem sharply: where AI genuinely adds value, where simple rules are enough, and which variables actually matter. You leave with a documented solution description and the KPIs the Sprint is judged against.
Data analysis
Before any modeling, we inventory and stress-test your data - quality, sampling, gaps, and whether the signal you need is even present. You get a data analysis report and concrete recommendations to improve collection.
Validation (PoC)
We build a thin proof-of-concept against a bounded dataset to test the idea fast and find its boundaries - where AI is reliable and where deterministic logic is still better - comparing results against your experts' judgment.
Deployment strategy
Finally, a concrete, phased plan to production: the edge-vs-cloud architecture, how it integrates with your stack, the effort to build the MVP, and the risks that remain. A path, not just a prototype.
Where the Sprint fits
The Sprint is the first step of a three-phase journey - Design, Develop, Deploy. You only commit to the build once the Sprint says it's worth it.
AI Design Sprint
Should we build this?
MVP
Can it run in production?
Productization
How do we operate and scale?
Proven results
A Sprint replaces a big, blind bet with a small, evidence-based one. A recent example:
A Design Sprint deliberately doesn't produce production-ready code. It produces certainty - where AI is worth building, where traditional logic wins, and on what terms to proceed to an MVP.
Where it applies
The Sprint format works whatever kind of AI the use case needs - we validate the load-bearing part against your data.
Predict & detect (ML)
Pricing, demand forecasting, anomaly detection, predictive maintenance, auto-calibration - anywhere value comes from scoring or forecasting structured or time-series data.
Understand & generate (GenAI)
Question-answering over your documents, report drafting, extraction from PDFs, support assistants - anywhere value comes from language and unstructured content.
Decide & act (Agentic)
Agents that pick tools and act in sequence: reconciliation, autonomous analysis, multi-step automation, with people in the loop for the high-stakes calls.
Mixed systems
Most real systems span types - an ML prediction with a GenAI explanation, a chat that also acts. The Sprint validates the load-bearing piece and the integration, not just the parts.
What you get
You walk out able to make the build-or-don't decision with confidence - and to act on it immediately.
The documentation is structured so it drops straight into a board paper or a Business Finland application.
