RACE Programming in commercial delivery
Real engagements run in the shape of RACE Programming: what was delivered, how fast, and what the model bills for. Client identities are withheld; the delivery facts are not.
AI-augmented supply-chain product
A fixed-price, end-to-end delivery for a US-based supply-chain client. A working prototype produced between sales calls, a foundation-level redesign absorbed mid-engagement, and four Definition-of-Done gates green on every story.
Read the case study →Two native apps to one React Native codebase
A three-person team rebuilt two separate native mobile apps as a single React Native app from nothing but the old source archives, producing a working, near-pixel-perfect demo in three working days, then one-day release cycles.
Read the case study →An embedded AI knowledge assistant
A two-person AI team shipped a production, document-grounded AI assistant for a 50+ office professional-services network, at an estimated ~3× the speed and ~⅓ the cost of a conventionally-staffed team.
Read the case study →Agent-to-agent data imports
A multi-office US property valuation firm consolidates data from every regional office without mandating a template. When the sender is an AI agent, the import service answers in machine-actionable findings, including a verdict on whose problem it is.
Read the case study →The ceiling of AI tools
Eighteen months of AI tools made one developer on the same firm's core platform 8.3 times faster at coding, window against window. The team gained 65%, so the next change was the team structure, not the tools.
Read the case study →An agentic workspace on every appraiser's desk
The same firm's appraisers got an agentic workspace on their desks. One data entry step fell from two and a half hours to twenty minutes, and the appraisers started building tools of their own.
Read the case study →A one-person orchestra, and the merges nobody does anymore
One engineer automated the upstream merge work that a fork with two dozen feature branches would otherwise demand from a team. Scripts plan, the agent resolves in a sandbox, and a human arbitrates only what genuinely needs it.
Read the case study →Agents that cannot leak credentials
Container isolation protects the host, not the secret inside it. This platform moves the credential boundary to the network, so the agent reaches every system it needs and still never holds a real token.
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