What we ship,
and what it changes.

A regulated services firm spent thousands of analyst hours per month reconciling data across legacy systems — error-prone, slow, and impossible to scale.

We deployed a workflow agent grounded in the firm's source-of-truth systems, with deterministic verification steps, full action logs, and a human-in-the-loop checkpoint for material exceptions.

• Throughput per analyst lifted significantly
• Every action replayable and auditable
• Exception rate reduced and trended week-over-week

An R&D-heavy organization needed to convert a backlog of internal research into customer-ready technical material without diluting accuracy.

We architected a multi-agent pod — researcher, drafter, reviewer, and citation-checker — coordinated by a supervisor with explicit role contracts and a shared evidence store.

• Lead time from research to publication compressed
• Citation integrity validated automatically
• Subject-matter experts shifted from drafting to review

A facilities operator needed to manipulate a long tail of items that defeated rule-based vision and traditional pick-and-place programming.

We built a perception stack combining classical CV with learned grasp prediction, validated in simulation, and deployed it with safe-fallback behaviors and an in-line data flywheel.

• Coverage extended to previously unhandled SKUs
• Cycle time held within operational targets
• Continuous improvement loop now owned by client team

A scale-up was building an internal agent platform and needed a candid technical assessment before committing to a multi-quarter roadmap.

Two-week deep dive: architecture review, eval coverage analysis, on-call interviews, and a written report with prioritized recommendations and a phased remediation plan.

• Clear buy/build/refactor decisions on every subsystem
• Eval debt quantified and scheduled
• Roadmap re-baselined against realistic risk