Approach.
Most enterprise AI work doesn't ship, or ships and doesn't work. The reasons cluster:
- The team isn't senior enough to make the judgment calls.
- The AI parts haven't been built to production standards before. The evals that tell you if it actually works are often the missing piece.
- The team is strong on AI development but doesn't know how to work with legacy systems.
- The requirements aren't well understood.
- The team knows the business but doesn't know how to build well with modern AI tooling.
The way we're set up follows from that.
Senior people, doing the work.
Most consultancies sell with partners and deliver with juniors. We don't. The people you meet in the first call are the people writing the code, reading the data, and pushing to production. No team behind the team.
End-to-end, including production.
We take engagements from the first scoping conversation through to running software. Not a slide deck, not a pilot for someone else to productionize, but the system that actually runs.
AI-native development.
Modern AI tooling changes what a small senior team can do, and we use it throughout the work. Used carefully it produces faster and more reliable systems, used carelessly it produces code that looks fine and breaks in production. Senior engineers are what makes the difference. When AI capability belongs in the product itself, we build that in too.
How engagements run.
A small senior team, usually two or three engineers, working an engagement from scoping through production and often into ongoing operation. Engagements are typically measured in months for first delivery and years for continuous stewardship of the system afterward. The greenfield production system shipped in twelve weeks. The incident management system ran for nearly two years.