Less Tooling. More Building.

Practical agent ops and observability for pragmatic, enterprise AI engineering teams.
Zeeq code review inbox showing findings for a pull request

Findings, not diffs — triaged by severity and ready to apply

Zeeq reviewer agent configuration with prompt editor and model tier

Specialized reviewers, tuned to your standards

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

Know which docs are shaping your code

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

Tool calls, knowledge reads, and reviews in one view

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

Severity and volume trends across your whole org

Zeeq session telemetry showing token usage and cost by model and user

What your agents cost, by model and by developer

Code reviews built for agents and humans

Every pull request gets a grounded review with findings sorted by severity, anchored to the exact file and line, and paired with the fix. Add what matters to a findings cart and let your agent apply it...or let your agent run the review in-loop before the PR.

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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. 

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.
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