Your codebase, explained to
every AI tool you use.
A deep scan of your repo that writes the context files your AI assistant is missing - CLAUDE.md, memory files, .cursorrules, AGENTS.md - done for you, delivered in days.
$ groundit scan ./your-repo
› mapping architecture ✓ services, data flow, entry points
› inferring conventions ✓ lint, tests, branching, naming
› surfacing gotchas ✓ the things that bite
› writing context files
CLAUDE.md ✓ memory tree ✓ .cursorrules ✓ AGENTS.md ✓
› done. your AI now knows the codebase
Works with your stack
// the problem
Your AI is coding half-blind.
Every AI coding tool is only as good as the context it's given about your codebase. Almost nobody gives it good context - because doing it right is tedious, expert work that never reaches the top of the backlog.
/init is shallow
The built-in generators skim package.json and emit a few generic lines. Your AI still has no idea how your services talk, where the auth lives, or which patterns are sacred.
Nobody has time to write a good one
A genuinely useful context file takes hours of an engineer who already knows the codebase cold - exactly the person with no spare hours. So it never gets written.
And then it rots
Even a great CLAUDE.md drifts the moment the code moves on. Multiply that by .cursorrules, AGENTS.md, and copilot-instructions, and keeping them honest is a losing chore.
// what you get
A complete onboarding package for your AI.
A real CLAUDE.md + memory files
Architecture, conventions, domain model, gotchas, test/build commands - the context an experienced teammate would give a new hire, written from an actual deep read of your repo.
Every format, from one scan
.cursorrules, AGENTS.md, and .github/copilot-instructions.md generated alongside CLAUDE.md - so every tool your team uses is grounded, not just one.
A setup-optimization report
What's missing or misconfigured in your AI-coding setup - ignored files, absent conventions, structure that confuses agents - with concrete fixes.
A 5-minute walkthrough
A short Loom walking through what the scan found and why it matters, so the value is obvious to your team - not just a pile of files dropped in a PR.
// the payoff
Less guessing. Fewer mistakes. Time back.
Grounded, always-current context changes how your AI works in your repo - and what it costs your team to keep it on track.
Time back for your team
The AI stops guessing, re-asking, and writing code that does not fit. Fewer dead-end attempts means less of your engineers time spent steering and re-reviewing it.
Fewer mistakes
Anchored to your real conventions, service boundaries, and gotchas, the AI writes code that matches the codebase - fewer hallucinated APIs and broken patterns to catch later.
Compact, loaded where it counts
Your whole codebase is compressed into a small, grounded brief - and per-area files load only when your AI is working there, so it gets real depth without burning the context window.
// the full scaffold
Not a file. A scaffold.
Groundit doesn't drop one CLAUDE.md and call it
done. One scan produces a complete, nested setup - a root file that maps the system and
@-imports a CLAUDE.md for every package, plus path-scoped coding
rules that load only where they apply. Here's the shape of what lands in your repo.
CLAUDE.md
root: architecture, how-it-works, gotchas - @-imports the tree below
the tree · a CLAUDE.md per area
-
packages/core/CLAUDE.md
the domain core / engine
-
packages/api/CLAUDE.md
the API + service boundaries
-
packages/shared/CLAUDE.md
shared types + utilities
-
apps/web/CLAUDE.md
the frontend app
-
services/CLAUDE.md
background workers / jobs
-
infra/CLAUDE.md
deploy + CI config
.claude/rules/ · path-scoped
-
components.md
apps/**/*.tsx
-
tests.md
**/*.test.*
-
migrations.md
db/migrations/**
-
api-handlers.md
packages/api/**
-
ci.md
.github/workflows/*
// one scan, every tool
Your team doesn't use one AI tool. Neither do we.
The same deep scan emits context in every major format at once. Anthropic will never write Cursor's files; Cursor will never write Claude's. We're vendor-neutral on purpose - one source of truth, every assistant grounded.
CLAUDE.md
Claude Code
.cursorrules
Cursor
AGENTS.md
Codex / agents
copilot-instructions.md
GitHub Copilot
// depth
The non-obvious things a quick scan misses.
A deep scan surfaces the tribal knowledge that usually lives only in senior engineers' heads - the stuff your AI most needs and is least likely to guess. The kind of thing it pins down:
▸ Where auth, sessions, and config actually live
▸ Which conventions are load-bearing - and which are just style
▸ The service boundaries you must route through, not around
▸ The build, migration, or deploy steps that only run in CI
▸ The silent invariants - money as integer cents, time in UTC, IDs that must stay stable
// no risk
Grounded, verified, guaranteed.
Every claim in your context files is checked against your source - and your whole codebase is distilled into a brief small enough for your AI to actually hold.
tens of millions
tokens of code in
hundreds of thousands
tokens of grounded context out
Across the real codebases we've run it on.
// the guarantee
If the package isn't materially better than what your tools produce on their own, you don't pay. The depth is something you can see - or you get a full refund.
// pricing
Priced by the size of your codebase.
Pick the tier that matches your repo. Not sure? Count it in one line:
git ls-files -z | xargs -0 cat | wc -l
// faq
Questions, answered.
Does my code leave my control? +
We work under NDA, and the scan runs against a read-only grant you can revoke the moment you have the files. Your code is never stored, resold, or used to train anything. Most clients add us as a temporary read-only collaborator, then remove us on delivery.
How long does it take? +
A few business days. We scan deeply, then hand-check every claim against your code and polish - so what you receive is accurate and ready to use.
Which AI tools do you support? +
CLAUDE.md (Claude Code), .cursorrules (Cursor), AGENTS.md (Codex and other agents), and .github/copilot-instructions.md (GitHub Copilot) - all generated from the same scan and consistent with each other.
How do I know which tier I need? +
Tiers are by lines of code. Run `git ls-files -z | xargs -0 cat | wc -l` in your repo for the total. If you are between tiers or on a monorepo, pick Enterprise and we will confirm scope before charging.
What if it's not useful? +
If the delivered package isn't materially better than what your tools produce on their own, you get a full refund - just ask within 14 days of delivery. The whole pitch is depth you can see - if you can't see it, you don't pay.
Give your AI the codebase it's been missing.
One deep scan. Every tool grounded. Delivered in days.