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Chat With Your Data: Confluence
Confluence's built-in search ranks by keyword match. If the document uses different terminology than your query, it doesn't surface. Engineers waste time searching for architecture decisions they know exist but can't locate. Support staff re-escalate tickets because the troubleshooting guide is buried under twenty search results.
A custom RAG layer ingests page content, page metadata, and Confluence's hierarchical space structure, then answers questions with direct quotes and page links. Ask "How do we handle database migrations in the order service?" and get the relevant runbook section, not a list of pages to scroll through.
Deploy as a Slack bot, a web widget embedded in Confluence, or a standalone interface. Works with Confluence Cloud. Data Center integration is available.
Tell us about your Confluence setup.
Six components that turn your Confluence wiki into a natural language knowledge base, with permission checking and citations built in.
The ingestion pipeline connects via the Confluence Cloud REST API, pulling page content, page metadata, space structure, and attachment text. The hierarchy is preserved so answers can reference the correct space, parent page, and child page in their citations.
Confluence permissions are respected at query time. Users searching the system only retrieve content from spaces and pages they have view access to in Confluence. The retrieval layer checks permissions against the Confluence user account before returning any result.
Confluence pages often contain headings, code blocks, tables, macros, and links to related pages. The indexing pipeline preserves section structure so an answer about a specific configuration step references the correct heading within the page, not just the page title.
Each answer includes the page's last-updated date. Pages not updated within a configurable threshold are flagged so users know whether a procedure document is current. This is particularly useful for runbooks and architecture decisions that become stale.
Deployed as a Slack bot, a web widget embedded in Confluence itself, or a standalone chat interface. The Slack bot is most common for engineering and support teams who spend their day in Slack. Questions are asked in context without switching tools.
For engineering teams, the system can reference related Jira issues when answering questions about known bugs, feature decisions, or architecture choices. Confluence decision pages linked to Jira epics surface the relevant ticket in the answer.
From API authentication to live Slack bot, the build sequence.
API authentication and space selection
We set up OAuth 2.0 authentication with your Confluence Cloud instance. You choose which spaces to include, typically engineering wikis, runbooks, product docs, and support knowledge bases. HR or finance spaces are excluded by default.
Page ingestion and chunking
Pages are pulled, converted to clean text (stripping Confluence macros and markup), and chunked by section heading. Long pages become multiple searchable chunks, each retaining the page title, space, and heading as metadata.
Permission map build
The system builds a permission map from your Confluence space and page restrictions. At query time, the user's Confluence identity is looked up and their accessible content is used as the retrieval scope.
Test retrieval on real questions
Before deployment, we test retrieval quality on a sample of actual questions your team asks. Common question types for engineering teams: architecture rationale, setup steps, API reference, runbook procedures. We tune chunking and retrieval to those patterns.
Deploy to Slack or web
The bot is installed in the relevant Slack channels, typically an #engineering-help or #it-support channel. Employees mention the bot or ask in a dedicated channel. The bot replies with the answer and a Confluence page link.
Delta sync for new pages
New and updated Confluence pages are picked up on a scheduled sync. The default is every two hours. For teams with high documentation velocity, near-real-time indexing via webhook is available.
Teams with maintained Confluence content and employees who need fast answers from it.
New engineers spend weeks reading old Confluence pages to understand why decisions were made. A search layer over the engineering wiki means asking "Why do we use event sourcing in the order service?" and getting the relevant ADR page immediately.
Support staff field the same questions repeatedly. If the answer is in Confluence but takes three minutes to find, reps either wing it or escalate. A Slack bot that finds the right Confluence page in seconds cuts resolution time and reduces escalations.
During an incident, nobody has time to read through Confluence search results. A natural language query interface means asking "What are the rollback steps for the payments service?" and getting the runbook steps immediately.
We’d rather decline than take a project that won’t deliver value.
Teams whose Confluence is primarily meeting notes
RAG search works well on procedural content: how-to guides, runbooks, architecture decisions, policy documents. If your Confluence is mostly meeting notes and ad-hoc pages with no procedural content, the search layer will not return useful answers. Invest in content before investing in AI search.
Confluence Data Center without API access
We build primarily for Confluence Cloud via the REST API. Confluence Data Center (self-hosted) integration is possible but requires network access to your Confluence instance and a different API integration path. Raise this in discovery if you are on Data Center. It is scopeable but adds complexity.
Tell us which spaces you want to search, your approximate page count, and whether you are on Confluence Cloud or Data Center. We'll reply within one business day.