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Senior applied AI tooling — embedded engagements, methodology audits, internal agent infrastructure setup, fractional architect retainer.

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What we build

Opinionated, LLM-native machinery for engineering workflows. Context engine, agent orchestrator, methodology — production-tested over 9 months on a 136K-line Rust codebase, agents shipping merged PRs daily. Not a framework, not a SaaS. Machinery designed for the LLM to operate within and improve. Configurability is overhead the LLM has to pay on every task; opinions remove discovery cost.

Engagements

What you get

  • Shipped commits in your language and conventions.
  • A methodology layer — CLAUDE.md, context docs, skills, lint rules — that lives in your repo and your team owns post-engagement.
  • ~3-5x velocity vs vanilla Claude Code during the engagement, after ~1.5-2 weeks of domain-onboarding setup.
  • Agents that improve the methodology with every task — your context docs grow, lint rules sharpen, role definitions adapt.

How we work

  1. Setup First 1-2 weeks: we onboard your domain into the context engine — ingest from your codebase, Confluence, internal wikis, Slack channels you share. Configure agent roles for your stack. Set up CLAUDE.md with your conventions.
  2. Engagement Our agents ship features through the same workflow as a human PR — planning, isolated worktrees, two-tier review gates. Every change goes through review before merge.
  3. Handoff Methodology layer lives in your repo. Your team + Claude Code can keep using and extending it after the engagement ends. No black-box dependency.

The fit matrix

Best fit
Embedded engineer engagements, Claude Code shops, dev-tools / AI-native gaming / mid-size product teams.
Partial fit
Copilot shops (methodology + context engine port; runner adapter is one-time work). Audit-shape engagements work across any agent tool.
Wrong fit
Maintaining LangChain pipelines, hourly-rate code dictation, procurement-locked enterprises requiring approved frameworks. We refer those out — different lane.
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