We work best where the data is messy, the edge cases carry the risk, and somebody on a shop floor or a trading desk has to act on the output. These are the environments we know.
Asset-heavy operations where downtime is the dominant cost and the data sits in systems that were never designed to talk to each other.
Where we typically start
Networks where a forecast is only useful if it reaches the planner in time, and where a single definition of on-time is harder to establish than it sounds.
Where we typically start
Environments where the edge cases carry the risk, the audit trail is not optional, and model governance is a regulatory conversation rather than an engineering one.
Where we typically start
Settings where data residency and confidentiality often rule out an external API call, and where clinical workflow reality decides whether anything gets adopted.
Where we typically start
Distributed infrastructure where forecasting error is expensive in both directions and field operations run on a different clock to the analytics team.
Where we typically start
Knowledge businesses where the constraint is expert time, and where the work is document-heavy, precedent-driven, and resistant to naive automation.
Where we typically start
The patterns above recur across industries — a number nobody trusts, a review process that scales only by hiring, a forecast that gets ignored. If your constraint looks like one of those, the sector label is rarely the obstacle.
The first conversation is diagnostic: what is actually blocking the outcome, and whether we are the right people to unblock it. If we are not, we will say so.