Documentation.AI is the AI-native documentation platform behind product docs at Capillary, Shiprocket, Seclore and more. Its agent reads what changed in each customer's tools and drafts the update. Scalekit powers the agent integrations behind it.
Documentation.AI hosts product docs, API references and help centers for software companies, with role-based access, versioning, localization and custom domains built in.
Its documentation agent watches the tools where product knowledge changes, drafts the edit and holds it until someone on the team approves it. Published pages are readable by people and by AI agents through an MCP server, Markdown and llms.txt.
"Our customers keep their context in hundreds of tools. Scalekit lets us cover them without building each integration ourselves, and that coverage is what wins us enterprise customers."
The documentation agent is only as useful as what it can read. A pull request gets merged, a ticket gets closed, a decision lands in a Slack thread, and the docs need to change. Each customer keeps that context in a different mix of code hosts, trackers, wikis, CRMs and support desks, and each of those systems needs its own integration, with an OAuth flow for every user who connects it.
Code hosts, issue trackers, wikis, CRMs, chat and support desks. Each with its own API, pagination and rate limits.
Enterprise buyers connect their own accounts, so each one needs its own consent flow, scopes and token lifecycle.
Large buyers check their tools are supported before they sign. When a public company went live on Documentation.AI, connector coverage is what kept the deal.
Documentation.AI built its first integration, GitHub, on its own, and it took a few weeks. Then the team signed up for Scalekit. Scalekit's engineers opened a shared Slack channel with Documentation.AI's engineering team that day, the few questions they had were answered almost immediately, and the first five integrations, Confluence and Jira among them, were live within a few hours.
A few weeks for one integration.
Questions answered by the engineers who built it.
Confluence and Jira among them.
"We turned on five connectors with Scalekit on day one. Our first integration, built ourselves, had taken weeks. It makes no sense to build agent integrations yourself anymore. Scalekit is one of the best solutions out there."
The day Documentation.AI signed up, Scalekit opened a shared Slack channel with its engineering team. Questions about providers, scopes and edge cases went straight to the people who build the connectors, and came back while the team was still building.
Builds the documentation agent
Supports every integration
"The moment we signed up, Scalekit added our team to a shared Slack channel. Every question we had was answered almost immediately."
After that, the documentation agent asks for context by connected account, Scalekit pulls a scoped credential and executes the call, and the result comes back for the agent to draft from. Tokens never enter the agent process or the LLM context.
The documentation agent requests recent changes from a code host, tracker or wiki, referring to a connected account rather than a credential.
It finds the connection for that provider, looks up the user's connected account on it, and pulls a scoped credential kept refreshed outside the agent.
Scalekit runs the scoped call against the customer's own tools and returns the result.
The agent drafts the change from fresh context, and nothing reaches the live docs until a person approves it.
Documentation.AI's agent already runs 25+ connections across support, issue tracking, knowledge bases and CRM. When a new customer runs a different stack, the team can turn on any of Scalekit's 500+ connectors and 20,000+ ready-to-call tools, or add a custom connector, without writing new auth code.
Each provider is set up once as a connection. Every user who links their account gets their own scoped, audited connected account on it, and one agent build serves all of them.
Tokens stay in an AES-256 encrypted vault and are injected at call time, never into agent code or LLM context.
Add a tool to an existing connector, or bring a new one over OAuth 2.0, API key or Basic auth.
500+ connectors with OAuth, token lifecycle and tool calling handled for every customer account, so your engineers can spend their time on the agent your customers asked for.
