What we can build for you.

Problem

Your data shouldn't have to leave the machine to be useful. Most tools force a cloud round-trip for things that should work offline.

Outcome

Browser extensions and local applications with verifiable data boundaries — storage you can inspect, backups you control, no silent uploads.

Typical engagement

Product definition → data-boundary design → extension/app build → live validation on real pages → release evidence package.

Problem

When something breaks in production, the first tool you reach for should leave the system under investigation unchanged. Recovery and diagnosis are different jobs.

Outcome

Read-only diagnostic tools that trace evidence to file and byte offset, with explicit interpretation limits instead of automated verdicts.

Typical engagement

Format research → parser with fixture tests → correlation views → safety model (hash-verified, loopback-only) → portable release.

Problem

Dense models on consumer hardware usually mean unusable speeds or dual-GPU cost. And benchmark TPS doesn't predict real agent throughput.

Outcome

Measured, reproducible inference recipes with workload-aware routing — every number carries its runtime, artifact, context, and measurement condition.

Typical engagement

Hardware envelope analysis → quantization/runtime matrix → agent-trajectory qualification → routing recipe + public benchmark provenance.

Problem

Long-running generative work collapses without persistent state: facts drift, resolved events reappear, and 'the model said so' isn't an audit trail.

Outcome

Generation pipelines that treat structured state as the source of record, check evidence before accepting canon, and block failed changes.

Typical engagement

State-model design → pre-commit checks → E2E continuation runs → reporting that clearly labels same-model validation.