Documentation is usually paid for in engineering hours: tracking changes, rewriting pages, chasing reviews, at loaded rates of roughly $75–120 an hour. Docify connects your repositories, generates and updates documentation automatically when the product changes, holds it to a quality gate, and keeps a human on every approval. The hours go down; the standard doesn’t.
Read-only access · your source code is never stored · free plan includes 5 generations · no credit card required.
Nobody signs an invoice for manual documentation, which is why it rarely comes up in a cost review. The spend is real all the same: engineers re-reading code they understood at merge time, releases queued behind writing, questions routed to the busiest people on the team, new hires ramping on pages that describe last quarter’s product. The interactive estimator on this page compares your current process cost with Docify Team at $79 per month billed annually ($99 monthly) with five seats included, using your own numbers.
Connect GitHub, GitLab, and Bitbucket — multiple accounts, organisations, groups, and workspaces, public or private — into one central catalogue, reusable across generation, automation, and standardization. Access is read-only and your source is never stored.
Docify writes first drafts from your real code. Select sources — repositories, Jira issues, Confluence pages, Notion pages, or OpenAPI specs — choose a document type (API reference, user guide, quickstart, release notes, and more), pick output formats, and generate. Preview each format, run the AI quality review, edit inline, and export to Markdown, PDF, Word, HTML, DITA, DocBook, and ePub.
A webhook fires on merge. A relevance filter decides whether customers are affected and skips internal-only changes. The affected section of the existing document is updated in place, never duplicated. A quality gate scores the result, then it either auto-publishes or waits for a human.
AI proposes; your team decides. Every automatic change arrives as a proposal with inline and side-by-side diffs. Edit any span, ask AI to rewrite it, apply a style guide, accept, reject, comment, request changes, or approve and publish — every decision versioned in a full audit trail.
Rebuild documentation written by anyone, in any state, to one house standard using reusable style guides, terminology rules, and your own instruction files.
Docify scores every document across weighted dimensions and holds anything below the publish gate. It also models AI search readiness — an estimate of how well assistants such as ChatGPT, Claude, and Gemini can find and cite the content. It is a signal you can improve, deliberately capped below 100% — never a ranking promise.
Docify maps the repository it reads — files, languages, frameworks, dependencies, endpoints, and connected sources — into a knowledge graph that carries the evidence behind every entry: how it was found, and which file it came from when a single file is the source. The AI writes with that map in front of it, and the whole graph exports as JSON in Docify’s own documented open format, built on open web standards. The graph marks which files Docify read and which it only saw listed, and it never describes the contents of a file it did not read. Included on every plan.
Every document carries approval status, full version history, side-by-side comparison, one-click restore, and an audit trail. Export the AI Quality Report as PDF, HTML, or PowerPoint.
Docify keeps technical documentation aligned with your product. Connect your GitHub, GitLab, or Bitbucket repositories, and Docify generates or updates documentation from your real source, validates its quality, style, links, and AI-search readiness, lets your team review and approve every change, and exports the result to Markdown, PDF, Word, HTML, DITA, and more.
Automation pipelines run on every merge or push via webhook. Docify decides whether a change is meaningful to customers, updates the affected section of the existing document (never a duplicate), re-scores it, and either auto-publishes or holds it for human approval — so the release and its documentation ship together.
No. Docify filters changes for customer relevance using repository rules, include/exclude patterns, metadata, style guides, and AI reasoning, and routes low-confidence decisions to a human. Internal refactors and implementation details do not become customer documentation.
Each document is scored across weighted dimensions — LLM readiness, structure, clarity, completeness, terminology consistency, readability, style-guide compliance, and link integrity — with an overall score, a publish-readiness verdict, and a one-click or reviewer-approved fix for each finding.
Docify evaluates the signals that help machines find, understand, and cite your content — titles, metadata, structure, clarity, and completeness — and estimates how ready each major assistant is to retrieve it. It is a readiness signal you can improve, not a guarantee of ranking on any platform.
When Docify generates a document, it also builds a knowledge graph: a map of what the document was built from — the repository files whose contents were read, the further files the repository listing showed, the languages those file paths prove, and the frameworks, dependencies, endpoints, and connected sources found in what was read — with the evidence behind every entry. Every entry records which kind of evidence it rests on, so a file Docify read is never confused with one it only saw listed, and the graph never describes the contents of a file it did not read. The AI writes with that map in front of it, and the whole graph downloads as JSON in Docify’s own documented open format, on every plan. Documents generated before this feature shipped do not have one, and Doc Sync documents are not covered yet.
We keep no copy of your source files. Docify reads a limited selection of files at generation time and sends them to Anthropic, our AI subprocessor, to write the document; the files themselves are never written to our database. The finished document is stored with your account and can quote short excerpts, because it is written from your code. You can revoke access at any time.
We do not yet publish measured customer savings, so we will not quote one. What we can give you is the arithmetic. The Team plan is $79 per month billed annually ($99 monthly) and includes five seats — roughly $16 per person. At a typical loaded engineering cost of $75–120 per hour, the whole subscription is covered by about 40–60 minutes of saved documentation work per month across the entire team. The cost estimator on this page runs the calculation with your own figures, and the honest way to check it is the free plan — run five documents against a real release and compare the result with what that release usually costs you.
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