Constraints, not logos.

Mission-critical AI in regulated defense environments. The work has to be explainable, and it has to be auditable after the fact.

We have built toward high-uptime production targets and split the delivery across product, QA, platform, and ML science.

Secure platforms for biomedical AI, where a silent change to a workflow can corrupt the result.

Evaluation gates protect data integrity. The same systems have produced a large reduction in manual analysis effort.

In the work we have done, medical-device software means serverless APIs, retrieval services that connect enterprise data to an interactive product, and release pipelines with regression tests.

A consumer platform with tens of millions of monthly users. The engineering was a core service migration, tighter database queries, and substantially lower request latency.

Unit and integration tests covered the services and the asynchronous pipelines.