Measure documentation ROI
Show which docs answer questions and deflect tickets.
Docs teams struggle to justify headcount because page views prove nothing about impact. When budgets tighten, documentation gets cut first — precisely because its value was never measured in outcomes.
Kelu's per-source analytics show citation counts for every source: which docs actually get used in answers, how many questions each source resolves, and where the deflected-ticket savings come from. Workspace analytics roll this up into questions per week, top sources, and gaps — an ROI dashboard for the docs team.
How it works
- 1
Every answer records which sources it cited — usage is measured at the source level.
- 2
Per-source analytics show doc/chunk counts, last sync, and citation counts.
- 3
Deflection and usage metering tie docs to resolved conversations.
- 4
The workspace overview rolls it up: questions per week, top sources, gaps, and time saved.
What you get
- Prove which docs resolve questions — outcomes, not page views.
- Attribute deflected conversations to the content that powered them.
- Show leadership a docs ROI dashboard, not a traffic report.
- Spot low-value content and high-demand gaps in the same view.
Features that make it work
Got questions?
Frequently asked questions
What does 'citation count' actually measure?
How many times a source's content was cited in AI answers — a direct measure of which documentation is doing the answering, unlike page views which only measure landing.
Can I see cost alongside value?
Partly. Analytics estimate the time saved by self-served questions, and traces record the tokens behind each answer. Kelu does not report spend in dollars.
How does this help justify docs headcount?
You can show resolved and deflected question volume attributable to documentation, which sources carry the load, and the gap backlog that more writing capacity would close — an outcomes-based case instead of a traffic-based one.
How long does setup take?
Most teams are live the same day. You connect a source — a docs URL, GitHub repo, Notion workspace, or a file upload — and Kelu crawls, normalizes, chunks, and embeds the content automatically. The website widget is a single script tag, and the Slack bot installs in a few clicks. There is no model to fine-tune and no infrastructure to run.
Are answers backed by citations?
Yes. Every answer from your docs includes a structured citations list with the URL, title, and excerpt of each source used, and the answer text cites sources inline. Users can click through to verify any claim against the original document — which is what makes the answers trustworthy enough for support, sales, and internal use.
Is my content used to train AI models?
No. Your indexed content, embeddings, and conversations are isolated to your workspace and are only used to answer your own questions. Content is never shared across customers, and data is encrypted at rest and in transit. Enterprise plans add OIDC/SAML SSO, a widget without Kelu branding, and a custom SLA.
Which knowledge sources can I connect?
24 source types: websites, GitHub (docs files, source code, issues, pull requests, discussions), Notion, Confluence, Google Drive, OneDrive, Zendesk (Help Center and solved tickets), Jira, Jira Service Management, Linear, Salesforce Knowledge articles, Salesforce Cases, Discourse, Stack Overflow for Teams, YouTube, OpenAPI specs, S3, curated Q&A, and direct file uploads (PDF, DOCX, XLSX, CSV, Markdown, TXT, HTML). Sources sync on a schedule, and GitHub sources can re-index automatically via webhook on every push.
What happens when the AI is not confident in an answer?
Answers are grounded in your sources. When they do not cover a question, Kelu returns a transparent, configurable fallback — typically pointing users to your support channel — instead of guessing. Those unanswered questions are logged in knowledge gap analytics so you can close the gap in your docs.
Related use cases
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Ready to measure documentation ROI?
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