AI Coding Copilots
A copilot that knows how your team actually writes code
Give coding assistants durable memory of architecture decisions, conventions, and prior review feedback, so suggestions match your codebase instead of the public average.
The problem
A copilot sees the open file and recent prompts. It has no access to the decision made in a design review six months ago, the convention enforced in every code review, or the library the team deliberately stopped using. So it suggests the statistical average of public code, and a reviewer corrects the same class of mistake repeatedly.
With MemorySync
MemorySync holds the decisions and conventions that live outside the codebase — in reviews, issues, and design documents — and returns the relevant ones as the developer works. Connectors keep that record current from GitHub and your documentation tools without a bespoke pipeline.
How it works
- 1
Sync
GitHub and documentation sources are indexed with per-source approval.
- 2
Extract
Decisions, conventions, and review outcomes become durable atomic facts.
- 3
Retrieve
The copilot pulls conventions relevant to the file and task at hand.
- 4
Suggest
Generated code reflects how this codebase is actually written.
The context a copilot cannot see
Most of what makes code correct at a given company is not in the file being edited. It is in the pull request where a pattern was rejected, the issue where a library was deprecated, and the design document nobody re-reads.
A copilot with only file context cannot know any of it, so it produces plausible code that a reviewer rejects for reasons the copilot had no way to anticipate. The same correction recurs, which is the clearest sign that the knowledge exists in the team but not in the tooling.
What to store
- Architecture decisions with reasoning. Why this datastore, why this boundary. The reasoning is what makes the decision applicable to the next case.
- Conventions that reviewers enforce. Error handling, naming, module boundaries — the rules that never made it into a linter.
- Deprecated patterns. What the team moved away from, and what replaced it.
- Recurring review feedback. If reviewers say it more than twice, it is a convention that has not been written down.
A copilot that suggests a deprecated pattern is not making a model error. It is being asked to work without information the team already has.
Why connectors do the heavy lifting
This knowledge already exists in GitHub, Notion, and Google Drive. The work is not authoring it — it is keeping an index current without building a sync pipeline.
MemorySync connectors handle that with per-source approval, so only permitted repositories and documents are indexed, and incremental syncs keep the record fresh as decisions change.
Project scope keeps repositories separate
A convention from one service is often wrong for another, and in agencies or platform teams the separation is contractual rather than stylistic.
Project scope in MemorySync keeps those corpora distinct while still allowing genuinely organization-wide standards to be shared, without running a separate store per repository.
Supersession is what keeps it accurate
Conventions change. When a team adopts a new pattern, the old convention must stop being retrieved, or the copilot will suggest both and the reviewer is back to correcting it.
MemorySync supersedes contradicted facts rather than ranking them slightly lower, so a replaced convention stops competing with the current one.
What you get
- Suggestions follow internal conventions, not public averages
- Architecture decisions survive team turnover
- Review feedback stops being repeated
- Deprecated patterns stop being suggested
- Shared context across every developer
- Project-scoped so repositories stay separate
Use cases
Build ai coding copilots with durable memory
Start on the free tier and test retrieval, scoping, and deletion against your own workload.
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