The intelligence context layer AI code you can
AI code you can
actually trust.
AI made writing code fast. Shipping hasn't caught up – AI hallucinates, guessing instead of
knowing, so the time saved writing gets spent reviewing. CoreDoc builds a map of your code AI
uses to get its facts straight – same question, same answer, traced to the source.
30-min walkthrough with a founder. Your codebase, not slides.
Logger.Error
billing.worker
webhook.worker
cron.refund
metrics.resolve
cache.validate
billing.list
invoice.verify
shipping.verify
search.parse
metrics.send
payments.resolve
payments.handler
✕
Summary:
Purpose:
Traced to source
Always fresh!
Works with: Claude Code
Cursor
Copilot
and any other MCP client.
Why good AI ships bad code.
Agents search your code before they write, but that's best-effort, not a guarantee. They read what looks relevant, not everything – so even a careful pass covers ~99% of what matters. Nothing tells the agent when it's seen enough – or what it never saw.
The misses are silent – and they compound. 99% per prompt sounds safe; fifty prompts later – a normal week – there's a 40% chance something slipped. Twenty of the twenty-two places a change touches, found and updated cleanly – the other two take down prod. And you can't review code nobody knew was in play.
A smarter model searches better (and burns more tokens) but still can't prove it found everything. In our eval the most expensive model found the same one place in six as the cheapest. And the odds worsen as your codebase grows. What's missing isn't effort. It's the map.
848
Right file, wrong lines
Across real issues, agents typically found the file – and still missed the exact code the fix depended on.
SWE-Explore benchmark, 2026 · 203 repos, 10 languages
66%
of 49,000+ developers say their #1 AI frustration is answers “almost right, but not quite”
Stack Overflow 2025
Time saved writing is re-spent verifying
Google DORA 2025
An endless stream of AI slop
Across 1,154 posts, the same three complaints recur: review burden, eroding trust, degrading codebases.
Study of Reddit & Hacker News threads, 2026
Sources cited for context; no affiliation or endorsement implied.
How good AI ships good code – with CoreDoc.
CoreDoc doesn't search your code – it reads all of it. A parser generated for your codebase resolves how
everything actually connects: every function, service, database table, every call between them.
It checks its own work.
CoreDoc double-checks the map against your code and goes back for anything it missed. What it can’t map, it
tells you – gaps are shown, not hidden.
Your AI answers from map.
Today every AI tool starts from zero on every prompt, searching file after file for the one that matters.
CoreDoc builds the map once. After that your AI checks it over MCP, from whatever tool you already use —
faster answers, fewer tokens, source attached.
One map for the whole team.
Every engineer’s work lands in the same map – one current picture instead of ten private ones and one system for
working with AI instead of everyone prompting their own way.
Product team
Ask how the product works in plain chat – same current picture as engineering.
Engineers
See what a change touches before they touch it – and their work lands in the shared map.
AI agents
Write code that fits your system – with receipts, from every tool over MCP.
Measured, not promised
We added the map.
Here’s what moved.
6×
coverage
more of each change found
−26%
time
answers come back faster
−58%
tokens
you pay less for the same work
Eleven question types, eight codebases, 437 runs — graded blind by a judge from the other vendor's family. Every number links to the eval.
“I actually trust what the AI writes now. I barely re-check.”
– engineering lead, pilot team
29 vs 94 the cheapest model with the map beats the strongest without it
6 of 6 models moved the same way on both graph questions Read the eval ›
Connect. Map. Ask.
repos on your machine → desktop app
Step one
Point it at your repos.
Install the desktop app. Parsing runs on your machine – your code stays where it lives. No extension, no account for the trial.
4,535functions 258entrypoints
map ready ✓ MCP live
Step two
Watch the map come up.
Spot-check it against code you know cold – and see its coverage report.
Claude Code
Cursor
Copilot
any client
Step three
Ask from anywhere.
One MCP connection – then every tool your team already uses answers from the same map. Not a new workflow: the same AI, finally on the same page as your team.
Not all maps are equal
Most tools map your code.
CoreDoc maps it right.
01
Built for your codebase – not for the average one.
Most tools parse with generic, pre-built rules – great until your stack isn’t what they expected. CoreDoc generates its parser for your codebase, then corrects it until it passes its own checks. Any language, any framework, monorepo or fifty repos.
02
A map of what your code does – not just what calls what.
Files, functions and call lines are the easy part. CoreDoc goes to the parts that break: entry points, database entities, cross-service requests, async flows – summarized from the bottom up, so every piece carries what happens beneath it.
03
It knows what it doesn’t know.
Every map ships with its coverage report – what’s mapped, what isn’t, stated up front. When the map can’t answer, you find out from the map, not from prod.
04
Your code stays where it lives.
Nothing to upload. Parsing runs on your machines; on the team plan, only the map reaches the server. Your keys, your choice of model – and the map moves with you if you ever switch tools.
From one laptop
to your own servers.
Local
FREEPROFor one engineer. Free for individual use – no card, no account for the trial.
the full map – parser + map live entirely on your machine
every map ships with its coverage report
works with any MCP client
Team
One shared map for the whole team. Paid, per-seat.
everything in Local
one shared map – always current, for every engineer
cloud sync – only the map goes to the server, code never leaves your machines
plugs into your CI/CD – the map updates itself on every merge
team analytics
Enterprise
Full deployment in your infrastructure, for teams that can’t let anything leave.
everything in Team – on your own servers
nothing leaves your infrastructure, ever
deployment scoped and run with your infrastructure team
support direct to our engineers
parsing runs where your code livesthe map stores structure, not sourcemetrics stay in your workspacethe map moves with youyour keys, your choice of model – on every plan
What our early adopter teams say.
“It saves me an hour a day, easily. Faster answers and I don't lose my train of thought.”
Frequently asked questions
No. Parsing runs where your code lives. The map stores structure – names, connections, file paths – not your source. On the Team plan, only the map reaches the server.
During map-building, code passes through the AI provider you choose – your keys, your model, hosted or local. We never see it. For full isolation, Enterprise runs everything inside your infrastructure.
Embeddings rank code by resemblance to your question. The map stores how code actually connects – so answers come from structure, not similarity and the same question gets the same answer every time.
Your editor indexes one repo for autocomplete and jump-to-definition. CoreDoc maps across repos and services – entry points, database entities, async flows – and serves the map to any MCP client, not one editor.
The parser is generated for your codebase, so mainstream stacks work out of the box. Unusual ones are exactly what the pilot is for – we scope them with you before you commit.
That's where it works best. On a cross-service trace, Claude Haiku with the map scored 94; Claude Opus without it scored 29. GPT Luna with the map scored 88 against GPT Sol's 38. The map supplies the facts the model can't reach; the model tier decides how well it reads them.
Our own eval: six models from two vendors, eight codebases, 437 runs, every answer graded blind against a hand-built answer key by a judge from the other vendor's family. Method, questions and scoring are on the Evals page — including the questions where the map doesn't move the score.
It rebuilds from your repos as the code changes, so it never drifts from reality. Freshness is reported next to coverage – you always know what the map knows.
All three names describe the same category, and CoreDoc fits each of them. Under the hood it is a code graph: a parser generated for your codebase resolves how functions, services and database entities connect, and that graph is served to any MCP client. People also call this a knowledge graph for coding agents, codebase memory, or code intelligence. What CoreDoc adds on top is a coverage report for every map and cross-repo edges — the calls between services that single-repo tools don't see.