What's the difference between Foundation and Org Scale?

Foundation ($15–35K) makes one codebase legible to agents — the context layer, maps, skills, and guardrails. Org Scale ($40–90K) adds the operating layer around it: MCP, evals, CI agent-gates, multi-agent workflows, observability, and team training. Most teams start with Foundation and step up.

Why not just write an AGENTS.md ourselves?

You can — and the files will get easier to generate over time. What you're buying is the judgment: which conventions actually change agent behavior in your codebase, the repo-specific skills, the guardrails, and the security boundaries that make agents reliable rather than confidently wrong.

Is this locked to one vendor?

No. The rollout is vendor-neutral by design — it works across Claude Code, Codex, Cursor, and Gemini, so you're never dependent on one tool.

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AI Coding Rollout

Claude Code, Codex, and Cursor made reliable for your whole team — the context layer, repo skills, evals, CI gates, and training.

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PriceFrom $15K

Timeline3–10 weeks (Foundation → Org Scale)

TermsFixed scope

We roll out AI coding your team can trust — the files agents read first, and the gates that catch them when they're wrong. Foundation writes the context layer: AGENTS.md, CLAUDE.md, DESIGN.md, codebase maps, repo-specific skills, and your test, PR, and security conventions. Org Scale adds MCP, evals, CI agent-gates, multi-agent workflows, observability, and team training.

Vendor-neutral by design: the same rollout makes Claude Code, Codex, Cursor, and Gemini reliable in your repo. You own every file, and your team is trained on all of it.

Week by week

01Week 1

Immersion

We read the codebase the way an agent has to — architecture, module boundaries, and the conventions that live only in your team's heads.

02Weeks 2–4

Foundation

We write AGENTS.md, CLAUDE.md, and DESIGN.md, map the repo, build repo-specific skills, and set the test, PR, and permission guardrails.

03Weeks 5–8

Org Scale

MCP and tool integrations, the eval and regression suite, CI agent-gates on every PR, multi-agent workflows, and observability.

04Weeks 9–10

Rollout

Team training, workflow redesign, and a runbook — then we walk the whole team through every file before handover.

Scope

What the engagement covers

AGENTS.md / CLAUDE.md

What we build

Stack, architecture, conventions
Build + test + deploy commands
What the model must never assume

What it unlocks

Agents stop re-guessing your conventions on every run — the first file they read is the source of truth.

What you receive

The rollout, delivered as real files and gates — not a wiki page.

The files agents read first, the skills that automate your recurring work, the guardrails that keep output on-pattern, and — at Org Scale — the evals, CI agent-gates, and observability that keep a whole org in control. A sample is shown; yours is written against your repo.

The files we write

AGENTS.md, CLAUDE.md, DESIGN.md, and the rules agents read before they touch a line.

context-layer/Sample

AGENTS.mdroot + per-package

CLAUDE.mdoperator runbook

DESIGN.mdtoken + component contract

skills/6 repo skills

.mcp/boundariesscoped + documented

A repo skill

Your recurring tasks, encoded as one-command skills any agent can run.

skills/ship-feature/SKILL.mdSample

1---
2name: ship-feature
3when: adding a user-facing feature
4---
5 
6## Steps
71. Scaffold under src/components/<feature>
82. Wire the route + nav (header + footer)
93. Add tests; run `bun run check`
104. Open a PR with the feature template

The PR guardrails

The checks that keep an off-pattern agent change from merging.

ci · on-patternSample

design-system linton-token

typecheckstrict

test coveragegate ≥ 80%

convention checkAGENTS.md rules

permission scopescoped

5 pass · 0 warn · 0 fail

Before / after

What the context layer does to agent output quality.

impact · 60 daysSample

On-pattern PRs

89%▲ from 41%

Review rounds

1.4▼ from 3.2

Rework

−58%

Time to merge

−44%

PlusA team walkthrough of every file · handover docs · 30 days of async Q&A while your team beds it in.

Fit

Built for

VP Engineering

Standardizing agent conventions across repos

One context standard every team inherits, instead of ten engineers each tuning their own.

Staff engineer

Owning a large, load-bearing codebase

A repo an agent can navigate reliably — so AI accelerates the team instead of adding review load.

Platform team

Making a monorepo agent-legible

Maps, skills, and boundaries that scale agent work across every package.

FAQ

Questions, answered

Common questions

Start here

Book a free scoping call.

We scope the exact number with you, and sign a mutual NDA before any code or data is shared.

Starts at$15K

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One letter, every week. Working systems — not hot takes.

Build logs, agentic engineering decisions, agent failures, evals, and what survives real users. Sent weekly, never more.