Oversight for Every Agent.
The observability layer engineering leaders use to see exactly how agents are used across every team, every tool, and every repo — activation, reads, cost, and outcomes, all in one place.
Where tokens are being spent, which features are being built, by whom
Insight and visibility into which inputs shape agent output and code quality
Zeeq tool calls, knowledge reads, and reviews in one view
Mixture-of-experts code review agents guided by the knowledge layer
Findings, not diffs, grounded by and citing the knowledge layer
Severity and volume trends across your whole org
Token and cost visibility per developer
Track token usage and approximate spend by model, by user, and over time across every harness your team runs — Claude, Codex, Cursor, and anything else that speaks MCP. Link token spend to actual value creation by feature.
Trusted by high velocity, agent-first engineering teams
Chander Ramesh
CTO at Motion
I'm a believer because I've experienced it. This is a boiling frog type situation - your code quality will slowly get worse over time and it'll be terrible without it.
One Pane of Glass, Every Agent
Whatever your teams run — Claude Code, Cursor, Codex, Copilot — Zeeq sits underneath and reports what's actually happening: which tools fire, which docs get read, which findings get fixed. No shadow AI, no blind spots.
Agent harnesses
Codex, Claude Code, Cursor, and OpenCode connect through the same enterprise layer.
Zeeq platform
Knowledge, reviews, telemetry, and skills stay consistent across every team.
Engineering systems
GitHub, documentation, traces, and metrics feed back into agent workflows.
Configure in one line
One line of MCP config and telemetry starts flowing immediately. No rollout project, no per-team integration work.
$ claude mcp add --scope project --transport http zeeq https://app.zeeq.ai/mcp
→ Authenticating with zeeq...
→ Zeeq MCP ready! 35 documents, 5 review agents, 78 memory clusters
→ Combobulating your feature following guidance...
→ Your new feature is ready ✓
→ Using zeeq code review with guidance...
→ Reviewer findings: 1 CRITICAL, 1 MAJOR, 2 MINOR. Here are the recommendations...
→ All findings addressed! ✓
→ Session telemetry recorded! ✓
Manage code review findings: https://app.zeeq.ai/web/code-reviews
Proof Over Promises.
Zeeq turns agentic engineering from a black box into numbers leadership can act on: who's using what, what it costs, where quality is trending, and whether your standards are actually being followed — across every team, every tool.
- Visibility across every agentTool activation, document reads, and section-level usage, broken down by user, by agent, and by repo, across every harness your teams run.
- Cost you can trace to valueToken spend by model, by team, and by feature, so leadership can tie AI investment to delivered work instead of a monthly bill.
- Quality and risk trends, over timeReview findings tracked by severity, repository, and team, so you see where quality is slipping before it compounds — not after it ships.
- Standards enforcement you can verifyPublish best practices once and see, with evidence, whether they are actually being read and followed across every repo — not just deployed and hoped for.
- Evidence-based tuningTelemetry shows what is working, so you adjust knowledge, guardrails, and agent behavior with data instead of guesswork.
- Fits the stack you already haveLow-ceremony rollout across Claude Code, Cursor, Codex, Copilot, and whatever else your teams run - one harness or ten; it just works.
Frequently asked questions
Pricing
Start locally for free, or request access for enterprise self-hosted and managed cloud deployments.
- Local self-hostOpen source, AGPLv3; run in your own local environment for personal use.FreeDeploymentHosting modelLocal machineBest forEvaluation, PersonalLicenseAGPLv3PlatformKnowledge layerCode reviewsTelemetry dashboardsLocalSupportAI readiness assessment--AccessOpenOnboardingDocsIssuesGitHub
- DeploymentHosting modelYour cloudBest forPrivate infrastructureLicenseCommercialPlatformKnowledge layerCode reviewsTelemetry dashboardsEnterpriseSupportAI readiness assessmentHands on workshopAccessBy requestOnboardingDedicatedIssuesPriority
- DeploymentHosting modelZeeq managedBest forManaged rolloutLicenseCommercialPlatformKnowledge layerCode reviewsTelemetry dashboardsEnterpriseSupportAI readiness assessmentHands on workshopAccessBy requestOnboardingDedicatedIssuesPriority
Stop Flying Blind on AI.
Zeeq gives engineering leaders telemetry on agent activity, spend, and code quality across every team — so you can govern AI adoption with evidence, not anecdotes.