For engineering teams of 5 to 50

AI Code Generator, Grounded in Your Repo

Generated code is only as good as the context behind it. Ask codebasechat where things live first, then generate code that fits your repo and survives review.

Start free See how it works
Engineer at two monitors with a code editor and a chat panel open in the evening
Why generated code goes wrong

The gap is context, not syntax

66%
of developers name "almost right, but not quite" AI output their top frustration (Stack Overflow 2025)
45%
say debugging AI-generated code is more time-consuming (Stack Overflow 2025)
46%
distrust the accuracy of AI tools, against 33% who trust it (Stack Overflow 2025)
What it adds

What an AI code generator can't see on its own

Six lookups that turn a plausible snippet into one your team would merge.

Find the existing helper

Ask whether a retry wrapper or date formatter already exists. Get the file and line before a generator writes a second copy.

Learn the local conventions

See how errors, logging and config are handled in the service you are editing, so generated code follows the same pattern.

Follow calls across repos

Trace a function through a monorepo split into packages, or six separate repos, before you change a signature.

Architecture docs that stay fresh

A map of how a service is wired, refreshed on each push, so prompts start from facts instead of memory.

Inside your editor

Runs in VS Code and JetBrains next to your generator. Ask a question without opening a browser tab or pinging a teammate.

PR context for reviewers

Summarizes what a pull request touches and which call sites it affects, so generated changes get a real review.

Workflow

How it fits next to your generator

Four steps, from a connected repo to a reviewed change.

  1. 1

    Connect your repos

    Point codebasechat at your Git provider with read-only access. On the self-hosted plan, code never leaves your infrastructure.

  2. 2

    Ask before you prompt

    Where is billing retried? Which module owns sessions? You get the file, the line and the reasoning in plain English.

  3. 3

    Generate against the answer

    Paste the helper name or convention into your prompt in Cursor, Copilot or Aider. The generator now works from your code, not a guess.

  4. 4

    Review with context

    Open the pull request and read the summary of affected call sites first. Spend review time where the change can break something.

Before you generate

Stop generating duplicates of code you already have

The most common failure of an AI code generator is writing a new utility next to one that already exists. It compiles, passes a quick read, and adds a second source of truth. Asking the repo first costs ten seconds and removes the whole class of problem. Codebasechat will not write the function for you. It tells you the function is already in the shared package, and who last changed it.

  • Answers point to the actual file and line, not a stale wiki page
  • Surfaces the existing convention before you write the prompt
  • Works on a repo you did not write and have not read yet
Laptop screen with a code editor showing a generated diff next to existing project files
After you generate

Review generated code with the full picture

A 40-file diff from a generator is where review quality drops, because every line looks reasonable on its own. Codebasechat summarizes what the change touches and flags the parts of the codebase it depends on. A reviewer unfamiliar with the service knows where to look first. It narrows where human review has to happen. It does not replace it.

  • Surfaces call sites a change affects, even across repos
  • Flags code that has had no recent owner
  • Keeps the human decision on the merge
Two engineers reviewing a pull request together on a large monitor
Different jobs

Generators write code. Codebasechat explains yours.

They are complements. The table shows which job each tool is built for.

JobCodebasechatTypical AI code generator
Writes new code from a promptNoYes
Answers where X is implemented, with file and lineYesPartial, depends on open files
Reasoning across several reposYes, on ProVaries by tool and plan
Summarizes what a pull request affectsYesRarely
Architecture docs refreshed on each pushYesNo
Self-hosted or VPC optionYes, on TeamVaries by tool

Pricing

Three tiers. No seat minimum on the free plan.

Free

$0

For individuals trying it on one repo

  • 1 repo indexed
  • 50 questions per month
  • IDE extension for VS Code and JetBrains
  • Community support
Start free
Most popular

Pro

$24 /mo per seat

For teams of 5 to 50

  • Unlimited repos, multi-repo reasoning
  • Unlimited questions
  • Auto-generated architecture docs
  • PR context for reviewers
  • Slack integration
  • Priority support
Start 14-day trial

Team

Custom

For 50+ engineers or regulated environments

  • Self-hosted or VPC deployment
  • SSO and audit logs
  • Dedicated onboarding
  • SLA-backed support
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Common questions

What is an AI code generator?
An AI code generator turns a prompt or a partial file into new code. Tools like GitHub Copilot, Cursor and Aider do this inside your editor. The output is only as good as the context the tool can see, which is why generated code often compiles but ignores how your repo already solves the same problem.
Does codebasechat generate code?
No. Codebasechat answers questions about your existing repos with the file, the line and the reasoning. It is built to sit next to a generator: you look up the right helper, convention or service boundary first, then generate against it, then review the result with PR context.
Why does generated code fail review so often?
Rarely because of syntax. In the Stack Overflow 2025 Developer Survey, 66% of developers named AI solutions that are almost right, but not quite as their top frustration. The usual cause is a missing convention: a duplicated utility, the wrong error type, an internal API called the old way.
Can I use it with Cursor or Copilot?
Yes. Codebasechat runs in VS Code and JetBrains, so you can keep your generator of choice and ask codebasechat where something is implemented before you prompt. There is no replacement step, and nothing to migrate.
Is my code used to train a model?
No. Each workspace gets a private index, and code never trains a shared model. On the Team plan the index can run in your VPC or on self-hosted infrastructure, with SSO and audit logs.
How many repos can I connect?
The free plan indexes one repo with 50 questions per month. Pro removes both limits and adds multi-repo reasoning, which matters when a feature touches several services.
How long does setup take?
Connecting a repo with read-only access and asking a first question takes under 15 minutes for most teams. Large monorepos index in the background, so you can start asking before the full index is complete.

Give your generator the context it lacks

Connect one repo and ask your first question in under 15 minutes. Free plan, no credit card required.

Start free See pricing