Engineering
inside the
constraint.
Three capability areas. Each combines senior engineering judgment with AI-native execution and a defined path to acceptance.
We build and deploy software and private AI inside the client environment.
Model
Systems.
Run useful models inside your own walls.
We deploy open-weight and frontier models in a client's cloud account, VPC, or hardware. The deployment is designed around the data boundary and the work the model must perform.
Model serving and application integration in the agreed environment.
Evaluation suites, inference audit trails, version pinning, and data residency requirements.
Our own work includes running 500B+ parameter open-weight mixture-of-experts models on single-workstation hardware using expert streaming and speculative decoding, and coordinating multiple models on shared hardware.
Record
Systems.
Software that preserves the transaction.
We build systems of record, payments and reconciliation software, and operating layers for teams whose work cannot be reduced to a dashboard. Correctness is designed into the workflow, the data model, and the acceptance tests.
Dispatch, service records, billing, repair orders, payroll readiness, and financial operations.
Records, permissions, failure paths, and human review points are explicit.
We agree on observable behavior before the build and verify the delivered system against it.
Field
Engineering.
Senior engineers close to the work.
We embed with the people who use and own the system. AI agents carry much of the build; senior engineers set architecture, inspect the work, resolve ambiguity, and remain accountable for what ships.
Fixed-outcome work, embedded teams, or advisory.
Direct access to operators and technical owners, with decisions captured as the system takes shape.
An accepted, deployed result against criteria agreed at the start.
Choose the problem, then the shape of the work.
Tell us the system, the constraint, and the deadline. We will tell you if we can take responsibility for the result.