Your product changes.
Your help center keeps up.

HelpCenter.AI keeps product documentation in step with the product. It checks your help articles against your app and your releases, finds instructions and screenshots that no longer match, and drafts updates for your team to review. It can also write new articles from first-hand use of your product, from walkthrough videos, and from questions customers couldn’t find answers to.

It replays the flows your articles describe inside your app, using a test account you provide, and compares what it sees with what the article says: button labels, locations, steps and screenshots. It reads release changes from GitHub to know which articles a change is likely to affect, and it watches reader signals such as searches that return nothing.

Every statement is labeled observed (seen during a replay), inferred (derived from a release change, not yet replayed), imported (carried over from your existing article) or unverified (couldn’t be checked, with the reason). An observed check records the date, build, environment and account role, because a check run as an admin on staging is not the same as what your customers see.

It’s built not to guess. Instructions come from what the agent did and saw in your product. Anything it couldn’t check is labeled unverified and left for a person to decide, and your team reviews every change before it’s published.

AI support agents resolve customer conversations and often suggest knowledge improvements from them. HelpCenter.AI maintains the articles those agents answer from, and checks them against the product itself. The two work well together. Better sources help, but answer quality also depends on retrieval, instructions and escalation, so it’s worth measuring separately. See the comparison.

Docs platforms host and publish documentation, and many now include an agent that edits pages from your repository, API definitions or reader analytics. HelpCenter.AI is built around a different source of evidence: your running product. It proposes reviewed updates and publishes them to the help center you already use.

A bounded check of your existing help center. You share its URL, we agree a sample of articles, and you get findings with the evidence behind each one, with structural issues kept separate from product-verified inaccuracies. Then we walk through one proposed fix with you. It’s free during early access. How the audit works.

Not to start. A public-URL audit needs only your help center’s address. To check steps inside your signed-in product you provide a test account; its password is kept in an encrypted vault and is never shared with the AI model.

At first, yes. Updates wait for approval, grouped by cause, so one decision covers every article a change touched. Once a kind of update has proven routine, you can let it apply on its own, and you can revert anything.

In early access: HelpCenter.io, with direct publishing, plus Markdown and llms.txt exports. Zendesk, Intercom, Crisp, docs-as-code via pull requests and an API are next, in the order pilot teams need them.

It helps with the part you control: accurate, well-structured, current articles and an llms.txt file that AI systems can read. No tool can guarantee that an assistant will cite you; accurate sources make a correct answer more likely.

It’s in early access with assisted onboarding, so we work alongside a small number of teams at a time. Request an audit to get started. Public pricing isn’t announced yet; early-access teams get founding terms.