[product]
A multi-agent review engine, built out into a full enterprise product.
Autometric starts with a serious review engine, adds Task Context when linked tickets exist, then extends that same flow into compliance, governance, analytics, and history.
Security reviewer
Secrets, auth boundaries, unsafe data handling.
Bugs reviewer
Logic flaws, unsafe edge cases, broken assumptions.
Performance reviewer
Hot-path regressions and wasteful operations.
Style reviewer
Consistency, readability, and maintainability.
Task Context reviewer
Linked bug and acceptance context when tickets exist.
Compliance reviewer
Framework-aware controls for in-scope repositories.
Verifier agent
Consolidates findings into one ranked review stream.
QA agent
Samples completed reviews to score accuracy without raising live review noise.
[product lanes]
Six product lanes. One review flow.
One diff enters review. Task Context can add linked intent. The rest of the product extends that same path instead of replacing it with disconnected systems.
[product lane]
AI Code Review
Multi-agent review fan-out for bugs, security, performance, style, and compliance with conditional Task Context, Judge, and QA layers.
Engineering leaders, staff engineers, and developers validating review quality.
[product lane]
Task Context
Linked bug and enhancement context from task systems that changes review behavior without adding workflow sprawl.
Engineering, platform, security, and compliance teams that need review aligned to the work item behind the change.
[product lane]
Framework Enforcement
Named framework enforcement at the PR gate with control mapping, evidence export, and fail-closed merge behavior.
Security, compliance, and platform teams running in-scope repositories.
[product lane]
Governance & RBAC
Eight built-in roles, scoped rollout, immutable audit history, and deployment choices that survive procurement.
CISOs, platform owners, procurement, and security architects.
[product lane]
Analytics & Insights
Acceptance trends, severity mix, cost per review, and rollout diagnostics for engineering leaders.
VP Engineering, platform leaders, and tool owners measuring value and trust.
[product lane]
Time Machine
Historical bug-introduction analysis that connects present failures back to the pull requests that introduced them.
Staff engineers, SREs, and security teams investigating regressions and incident origins.
[review first]
The multi-agent fan-out is the baseline.
If the system does not catch real bugs, security flaws, and performance regressions, no compliance story will save the rollout. That is why the AI Code Review lane is explicit instead of implied, and why the architecture fans the same diff out into specialists before it returns one ranked review.
[task context]
Linked work items can change review behavior.
Bug tickets can demand regression coverage. Feature and task tickets can demand acceptance coverage. Task Context keeps the review aligned to why the change exists.
[compliance stays close]
Framework enforcement is layered onto the same review stream.
Compliance Engine adds named controls, evidence, and fail-closed behavior where scope requires it without turning the rest of the reviewer into checklist theater.
[enterprise fit]
Governance, analytics, and history make rollout durable.
Governance keeps procurement comfortable, analytics help engineering trust the system, and Time Machine extends the review story into incident and regression analysis.
[next steps]
Need to start with review quality or task-linked proof?
We can show the review engine first, the task-aware flow first, or the compliance layer first depending on who is in the room.