World Models from Code

An incremental, self-learning agent world model accumulated from each PR that powers agent-first teams.
$ claude "Let's build something amazing!"
→ Using zeeq to find relevant canonical guidance...
→ Identified key guidance: 2 documents, 7 sections, 4 code snippets, 15 memories
→ 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! ✓
Manage code review findings: https://app.zeeq.ai/web/code-reviews
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A cheat sheet for coding agents.

Zeeq plugs into your existing workflows seamlessly and incrementally assembles a world model which acts like a cheat sheet of your product and codebase by accumulating and assimilating knowledge, one PR at a time.
  • Low-friction, faceted code reviews
    Mulit-faceted, agentic code reviews for the AI era that focus on actionable feedback reinforced by the accumulated knowledge base.
  • Indexed, searchable world model
    Each code review incrementally builds the shared, semantic world model of your product and codebase by mapping features and lexicon to code.
  • Targeted retrieval
    Efficient, targeted, semantic retrieval of only the relevant sections of knowledge and code snippets that improve agent adherence and performance.
  • Full visibility into world model interaction
    See which documents and snippets are 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-friction and operate in agentic teams 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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