Living documentation for product and delivery teams. ScopeDocs connects Slack, tickets, docs, and calls into a sourced project record, and a knowledge graph over that work, so people and agents can open answers with sources attached.

How does ScopeDocs work?

Connect your tools with read-only OAuth, stamp decisions as work happens with citations back to the original PR, thread, or ticket, and ask with proof attached. Humans confirm what matters before agents treat knowledge as settled.

What sources can you connect?

Slack, GitHub, Jira, Linear, Confluence, Google Drive, Notion, Fathom call notes, Datadog, Microsoft Teams, monday.com, Supabase Database, and more, across the tools your team already lives in.

Can I use ScopeDocs with AI agents?

Yes. People and agents read the same record. Agents ask what your team asks, with citations, not a stale paste. Cursor, Claude Code, ChatGPT, and custom stacks connect over MCP.

Do you offer an MCP server?

Yes. Through MCP, agents in Cursor, Claude Code, ChatGPT, or your own stack can pull current decisions and proof at runtime from the same cited workspace record humans use in ScopeDocs.

Your workspace content stays in your workspace. We do not train public foundation models on your connected data, and we do not sell that data. Permissions follow the tools you already use. See Trust and the Privacy Policy for the full picture.

Does ScopeDocs work with private or enterprise GitHub?

Yes. ScopeDocs works with private GitHub repositories and GitHub Enterprise. Access is read-only via OAuth and follows your existing repo permissions.

How does pricing work?

Pricing fits team size, sources, and agent use. Request a quote on the pricing page or book a demo and we will match you with the right plan. There is no public fixed dollar price list because every workspace footprint differs.

AI documentation for engineering teams

Last updated August 2026

AI documentation for engineering teams means using generative models and coding agents to create, update, and retrieve technical context, while keeping every material claim source-linked. The goal is faster onboarding and better agent answers without trusting fluent prose that cannot be verified.

The problem AI docs must solve

Engineering knowledge is scattered across pull requests, tickets, Slack threads, design notes, and call recordings. Traditional wikis ask humans to copy that knowledge into pages. Most teams eventually abandon the wiki because upkeep never keeps pace with shipping. Generative tools make it easy to draft pages from scraps of context, but draft quality without provenance is a new failure mode: confident, outdated, or invented statements that look like documentation.

Effective AI documentation has two jobs. First, reduce the cost of capturing what already happened in tools. Second, keep citations attached so people and agents can open the original PR, ticket, or thread. ScopeDocs is living, source-linked documentation built for that pair of jobs, with agents querying the same record over MCP and integrations using read-only OAuth.

What good AI documentation looks like

Grounded in work: generated or updated from GitHub, Linear, Slack, and related systems
Cited by default: claims link to originating sources, not orphaned summaries
Human-confirmed: teams approve what agents should treat as settled truth
Agent-accessible: the same record is available in coding tools over MCP
Least-privilege connectors: read-only OAuth so indexing does not imply write access

Where AI documentation helps most

Onboarding, on-call, delivery handoffs, and agent coding loops all need current architecture and decision history with sources. See what is living documentation, what is source-linked documentation, and MCP for engineering documentation.

Adoption pattern that works

Start with tools you already use: connect a small set of high-signal systems first
Stamp decisions as they happen: capture claims with who decided and where evidence lives
Confirm before treating as settled: keep humans in the loop for production-critical claims
Connect agents second: once the cited record exists, expose it over MCP to coding clients
Measure trust, not page count: success is fewer wrong answers and faster verified lookups

AI docs vs wiki paste and raw RAG

Dumping a wiki into a model is not an AI documentation strategy. Neither is retrieving unmarked chunks from a stale export. Teams need living updates plus citations. Compare approaches in generative documentation vs wikis and wiki vs living documentation.

FAQ

Will AI documentation replace technical writers?

It changes the workload. Writers and tech leads spend less time chasing stale pages and more time confirming decisions and designing verification. AI handles capture and retrieval; humans own accountability.

Is pricing fixed for AI documentation products?

ScopeDocs pricing is quote-based for team rollout. See pricing rather than assuming a public dollar amount.

How do we keep AI docs secure?

Prefer read-only OAuth, workspace-authenticated agent access (no public anonymous MCP for private context), and clear indexing policies. Review privacy and product.