Hypervector
prelint
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Itโs a non-negotiable that shipped code matches product specs, not just that it passes code review. When AI agents move autonomously and fast, code drifts from specs, business rules, and compliance expectations. That drift shows up as rework, missed deadlines, and features that technically work, but break how the product should behave.
prelint reduces that drift. It synthesises your specs, tickets, emails, call transcripts, and meeting notes into a product knowledge graph and checks every pull request against those decisions before it merges, so you see which changes quietly contradict the spec while there is still time to adjust. You spend less time reโopening tickets, fixing last minute issues, or rolling back work that should never have shipped.
Not another tool in your tech stack: your team keeps its current GitHubโbased workflow and documents the expected behaviour where it already exists. prelint turns those decisions into checks that run with your existing pipeline and review flow. Leaders keep control over what is allowed to ship without adding more meetings. Developers and agents keep moving at the speed the business expects, inside clear boundaries that protect the product and your compliance workflows.
Hypervector prelintprelint's answer:
prelint is for productโled engineering teams using AI coding agents, where product, engineering, and compliance leaders want the product knowledge they already capture in specs, tickets, and calls to automatically govern what ships through CI and GitHub pull requests.
prelint's answer:
prelint connects to your GitHub repositories and ingests the specs, tickets, call transcripts, emails, and meeting notes that describe how your product should behave, building a product knowledge graph from them. That graph defines guardrails for AI coding agents and developers, which prelint enforces as checks across CI, CLI, and pull requests so product drift is caught before merge.