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Where we work
Same mathematics. Completely different projects.
What separates a fraud model from a crop-yield model is not the algorithm. It is knowing which errors are survivable, who signs off, what the regulator expects to see, and how the data actually gets recorded.
AI in Finance
Credit decisioning, fraud, AML alert triage and reconciliation — built to survive an audit, not just a test set.
AI in Retail
Demand forecasting, assortment, pricing and recommendations across store and online inventory.
AI in Manufacturing
Visual inspection, yield analysis and predictive maintenance on lines that were not designed for sensors.
AI in Transportation
Routing, ETA prediction, fleet utilisation and fuel accountability under real road conditions.
AI in Education
Assessment support, dropout risk and administrative automation, with strict limits on how student data is used.
AI in Healthcare
Documentation, triage support and operational forecasting — clinical decisions stay with clinicians.
AI in Agriculture
Yield estimation, disease detection and advisory systems that work on a low-end phone and a weak signal.
AI in Insurance
Claims triage, fraud detection and underwriting support with the evidence trail regulators ask for.
AI in Real Estate
Valuation models, lead qualification and portfolio analytics on famously inconsistent property data.
AI in Media
Catalogue enrichment, discovery, moderation and audience analytics at library scale.
Other sectors
Logistics, energy, legal, hospitality, public sector and non-profits — the sectors we work in that do not yet warrant a page of their own.
01 / Why sector matters
Three things that change with the industry.
- What an error costs
- In lending, a false negative is a written-off loan. In diagnostics it is a missed condition. In retail recommendations it is a slightly worse afternoon. These are not comparable, and they determine the entire tuning strategy.
- Who has to be convinced
- A model that a data scientist trusts and an auditor rejects has not shipped. In regulated sectors we design for the review conversation from the start — feature provenance, explainability, documented decision logic, retained evidence.
- How the data was really recorded
- Every operational dataset carries the fingerprints of the process that produced it: the field staff use as a workaround, the status nobody updates, the back-dating at month end. Sector experience is largely knowing where to look.
Next step
Your sector not listed?
The list reflects where we already have domain context, not the limit of what we can build. If your problem is well-defined and the data exists, the sector is rarely the obstacle — tell us about it.