Less Tooling. More Building.

Practical agent ops and observability for pragmatic, enterprise AI engineering teams.
Zeeq session telemetry showing token usage and cost by model and user

Where tokens are being spent, which features are being built, by whom

Zeeq library metrics showing most-read documents, sections, and snippets

Insight and visibility into which inputs shape agent output and code quality

Zeeq overview dashboard with tool call, knowledge base, and review metrics

Zeeq tool calls, knowledge reads, and reviews in one view

Zeeq reviewer agent configuration with prompt editor and model tier

Mixture-of-experts code review agents guided by the knowledge layer

Zeeq code review inbox showing findings for a pull request

Findings, not diffs, grounded by and citing the knowledge layer

Zeeq code review telemetry charting findings by severity, repository, and origin

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.

Try for freeGet started

Trusted by high velocity, agent-first engineering teams

Chander Ramesh
Gary Chao
Charles Chen
Tom Österlund
Lefan Tan

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. 

Configure in one line

Get up and running in just a few minutes with a single line of configuration
$ 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
Learn about configuration

Knowledge base + code reviews + memories + observability

Zeeq helps enterprise teams move faster with AI agents while producing higher quality code by combining a canonical knowledge base, mixture-of-experts code reviews, a "world model" memory system, and the telemetry to prove that it works.
  • Indexed, chunked, searchable knowledge base
    Use a shared knowledge base that grounds both code review and code generation, ensuring that agents are following best practices and enterprise standards.
  • Mixture-of-experts code reviews
    Use out-of-the-box and easy to tune agent code reviewers that are grounded in the same knowledge base and used consistently in your coding loop and in your PR.
  • Targeted retrieval
    Efficient, targeted, semantic retrieval of only the relevant sections of knowledge and code snippets that improve agent adherence and performance.
  • High visibility and observability of outcomes
    See that text in your corpus is actually shaping agent output and your codebase to keep your team aligned with best practices.
  • Iteratively self-learning
    Compiles a deep, semantic understanding of your product, your features, your code as it reviews code so agents actually know what to build
  • Built for teams
    Designed to be low-ceremony, easy to adopt, and operate in agentic teams that are using heterogenous agent harnesses, AI-enabled runtimes, and LLMs

Frequently asked questions

What if everyone on your team could ship code like your most senior engineers?

Zeeq is the tool that lets agents write smarter code that bridges a semantic understanding of your product with a technical understanding of your codebase and your enterprise ecosystem so every member of your team can ship confidently with AI.
Try for freeGet started