Clevo connects to the tools your team already uses, builds a living model of how your product works, and turns it into grounded answers, ready-to-build tasks, and PRDs.
Jira
Confluence
Notion
Linear
GitHub
GitLabEvery team has the two or three people who know how the product actually works. Everyone else borrows their memory.
Every “how does this work today?” interrupts the most expensive person on the team. Every ticket written without that knowledge comes back as clarification rounds, midnight rewrites, or the wrong thing built.
The context exists — in Slack threads, in old tickets, in docs nobody trusts, in heads. It just doesn’t exist anywhere as a whole.
The expensive part isn’t writing tickets or answering questions. It’s reconstructing how the product works — again, and again, and again.
Clevo maintains a structured model of your product: business logic, architecture, entities, integrations, user flows, and team conventions. Not a pile of documents — a model. Every claim in it is traceable to its source, tracked for freshness, and verified by your team.
Everything Clevo says is grounded in that model. When it answers, it answers about your product. When it writes, it writes in your team’s terms.
“How does refund eligibility work today?” “Which services touch the loyalty balance?” Clevo answers from your product model — in your terminology, with sources attached.
Paste a Slack message or a one-line request. Clevo asks what’s missing, then produces a structured ticket: title, business rationale, affected areas, acceptance criteria.
Describe the feature. Clevo drafts a PRD grounded in your real entities, constraints, and flows — or take a PRD you wrote and let Clevo pressure-test it: missing edge cases, contradictions, integrations you didn’t think about.
Should customers be able to move any scheduled payment, or only ones that failed to debit? Your payment provider allows date changes only before debit initiation — should moves be blocked after that point?
The same flow scales up to a full PRD.
That’s why Clevo can ask the right questions before work starts. A generic model doesn’t know enough about your product to know what it doesn’t know.
Jira, Confluence, Notion, Linear, GitHub, GitLab. Read-only access — you choose which projects and spaces.
It extracts your entities, flows, architecture, integrations, and conventions. Conflicts between sources and gaps in coverage become open questions — not silent guesses.
Approve as is, or edit. Every claim links back to its source, so review is fast — and after it, the model is something your team has verified, not something an AI hallucinated.
Closed tickets, merged PRs, and updated docs flow into the model. Staleness gets flagged, not accumulated.
Turn intent into requirements engineering can build — without a week of clarification ping-pong.
Stop rewriting tickets and re-explaining the architecture. Capture it once; the model does the explaining.
Understand not just what to build, but why it matters and what it touches.
Product knowledge stays in the company — not in the heads of whoever built version one.
A custom GPT reads documents and summarizes them — including the outdated ones, with no way to tell you what’s missing. Clevo builds a structured model: entities, flows, constraints, conventions — verified by your team, tracked for freshness, with every claim traceable to a source. That structure is what lets it catch what’s not written, not just retell what is.
Read-only, scoped to the projects and spaces you choose, revocable at any time. Clevo never writes to your tools.
Expected — that’s most teams. Clevo cross-checks sources; conflicts and gaps become open questions for your team to resolve during review. The model is what you verified, not what the wiki claims.
Only if you connect a repository, and that’s optional. Code access improves technical precision — service ownership, data models, edge cases — but Clevo works from trackers and docs alone.
No. Work stays in your tracker, docs stay where they are. Clevo operates before the tracker: it’s where questions get answered and work gets shaped.
No. Your workspace is isolated, and your data is never used for model training — not by us, not by the LLM providers we route through.
Connect your sources and give your team a product that can explain itself.
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