// work

Four practices, one standard

Everything we ship is built to still make sense a decade later — whatever the practice, whatever the stack.

01

Product engineering

Full-cycle product work: interface design, frontend, backend, mobile, and the release pipeline — one accountable team from first sketch to production traffic. We're at our best when the product is the business and it has to be right.

  • web apps
  • react native
  • design systems
  • MVP → scale

Typical builds

  • A SaaS product from zero to paying customers
  • Customer portals over existing business systems
  • Mobile companions to established platforms
  • Design-system rebuilds of aging UIs
02

Platforms & APIs

The load-bearing layers nobody screenshots: APIs, data pipelines, integrations, auth, billing, infrastructure-as-code. Fast, observable, and boring in the best possible way — the part of the stack that should never be exciting.

  • APIs
  • event pipelines
  • integrations
  • IaC & devops

Typical builds

  • Public API layers with real versioning discipline
  • Integration meshes across SaaS and legacy systems
  • Data pipelines feeding analytics and ML
  • Cloud re-architecture with cost visibility
03

Modernization

Legacy systems rebuilt in place. Strangler-fig migrations, parallel runs, and evidence-based cutovers — the business never stops while the engine is swapped. Twenty-five years of shipping means we've modernized systems younger than us.

  • rescues
  • migrations
  • refactors
  • parallel runs

Typical builds

  • Rescue of a stalled or inherited codebase
  • Incremental re-platform off end-of-life stacks
  • Monolith decomposition where it actually pays
  • Documentation recovery for orphaned systems
04

Applied AI

Machine intelligence wired into real products — retrieval over your data, agents that do real work, automation with evals proving it works. No demos that die in production; features that earn their keep or don't ship.

  • LLM features
  • retrieval
  • agents
  • evals

Typical builds

  • Retrieval-augmented search over company knowledge
  • Workflow agents with human-in-the-loop controls
  • Document-processing automation with audit trails
  • Eval harnesses that keep AI features honest

Which one is your build?

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