Engagements.

Incident management system at scale.

A multi-million line incident management system was under active development but increasingly hard to work with. The mobile app, sending heavy telemetry from the field, was unreliable. Cloud costs were ballooning. Server-side changes were destabilizing the platform, slowing the pace of new features.

We took end-to-end ownership for nearly two years and shipped through:

  • Stabilizing the mobile app under heavy telemetry load
  • Reducing cloud infrastructure costs by 50%
  • Stabilizing the server side so new features could land safely
  • Moving deployment to infrastructure-as-code
  • Introducing AI automation incrementally into the operational workflow

What it tells you: we can take ownership of a large incident management system, stabilize it under real production load, and ship new capability through that environment.

Greenfield production system.

A greenfield production system for an online video auction business. Built from scratch: live video streaming, Stripe Connect payments, shipper integrations, catalog management, social features. Two engineers, twelve weeks, about 100,000 lines of code, 1,800 tests covering the operational paths, shipped to production with full deployment automation.

What it tells you: a small senior team can take a complex production system, live video, payments, integrations, catalog, from nothing to shipped in a matter of weeks, with the test coverage and deployment automation to run it for real.

Operational intelligence, high-precision polymer manufacturing.

A chemical manufacturer's high-precision polymer line had a recurring out-of-specification problem worth roughly $0.5M to $1M per month in lost revenue. Previous statistical analysis hadn't produced changes that closed the gap.

We ran an AI-accelerated re-analysis across 500 process variables and several years of historical data, in days rather than months, exploring far more hypothesis paths than the prior manual approach had covered.

The findings: a specific pump on one line emerged as the largest potential lever in the analysis, though a within-campaign association needs a designed plant test before any operational change. A post-cleaning transient was driving roughly 20% of the total loss, with the first 24 hours running 3–4× baseline — the most evidentially solid finding, actionable on both lines. A separate set of data and instrumentation defects were fixable without modeling. We separated which findings were strong enough to act on from which were leads needing further validation.

What it tells you: AI-accelerated analysis explores far more of the option space than manual work can cover, in a fraction of the time, and produces a more honest, sharper diagnosis as a result.