World Models from Code

Low-friction, integrated, self-assembling world models of your product for coding agents.
$ 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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Elevated AI coding performance for agentic engineering teams.

Zeeq plugs into your existing workflows seamlessly and incrementally assembles a world model 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 anyone 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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