Knowledge Base Article Freshness Audit
Internal wikis grow for years and almost nobody prunes them. An article about the VPN setup process gets written once, the VPN vendor changes eighteen months later, and the article keeps surfacing at the top of search results with instructions that no longer work. Employees follow it anyway because it looks authoritative, then open a help-desk ticket when it fails, which is slower and more frustrating than if the article simply hadn't existed. Nobody owns going back through hundreds of pages to check which ones are still accurate, so staleness accumulates silently until it shows up as wasted time and bad first impressions for new hires.
STARTING PRICE
From €99
Starter tier · Single-workflow automation, one core integration, fast turnaround.
Get a quote →Saves roughly 3-5 hrs/week of manual wiki review plus fewer tickets from employees following dead instructions.
How the automation works
We scan your wiki or knowledge base on a schedule and cross-reference each article against signals of staleness: last-edited date, references to tools or vendors that have since changed, broken internal links, and a comparison against related tickets where the documented steps didn't match what support actually told the requester. Articles that reference deprecated software, have gone untouched for over a defined threshold, or keep generating tickets despite existing are flagged with a specific reason, not just a generic 'old' label, and routed to the listed owner or a fallback reviewer if the owner has left the company. Reviewers get a short queue instead of a manual crawl through the whole space, and each resolved flag updates a freshness baseline so re-flagging doesn't happen on the same article every cycle.
Process flow
- 01
Scan the knowledge base on schedule trigger
A weekly or monthly sweep pulls every article's metadata: last edit date, author, listed owner, and page view or ticket-deflection stats where available.
- 02
Cross-check content against current reality ai
Articles are checked for references to vendors, tools, or policies that have changed, and for broken internal links pointing at moved or deleted pages.
- 03
Correlate against recent tickets ai
Support tickets referencing the same topic are compared against the article's stated steps to catch cases where the documented process no longer matches reality.
- 04
Flag with a specific reason ai
Each stale article is labeled with why it was flagged — outdated vendor reference, broken link, or ticket mismatch — instead of a generic staleness score.
- 05
Route to the right owner output
Flags go to the article's listed owner, falling back to a department lead if that person has left the company or changed roles.
- 06
Track resolution output
Once an article is updated or archived, the freshness baseline resets so the same page doesn't reappear in next cycle's queue for no reason.
Inputs
- Wiki or knowledge base export/API access
- Support ticket history
- Current vendor and tool inventory
- Article ownership list
Outputs
- Prioritized stale-article queue with reasons
- Broken internal link report
- Owner-routed review tasks
- Freshness trend report over time
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Last-edited date alone is a weak signal — an article can be technically 'recently edited' because someone fixed a typo while the actual instructions underneath rotted years ago, so staleness detection has to weigh content signals, not just timestamps.
- Flagging every article that mentions a renamed tool creates alert fatigue fast if the rename was cosmetic (a vendor changing its logo, not its workflow) — the check needs to distinguish surface changes from functional ones or reviewers start ignoring the queue.
- Archiving an article that's still linked from dozens of other pages or from external customer-facing docs breaks those references silently — flagged-for-archival articles need a backlink check before removal, not just a delete button.
- Owner-routing fails quietly when the listed owner field is wrong or blank for a big chunk of the wiki, which is common in wikis that predate any ownership convention — the fallback routing logic has to handle 'no owner' as the normal case, not the exception.
Frequently asked questions
Does this rewrite outdated articles automatically?
No, it flags them with a specific reason and routes to the right person — content changes still go through your normal review and editing process.
How does it know an article is actually wrong, not just old?
It cross-references vendor and tool changes, checks for broken links, and compares the article against recent support tickets on the same topic to see if the documented steps still match what's actually happening.
What happens to articles with no listed owner?
They route to a fallback reviewer — typically a department lead — so gaps in ownership data don't mean the article never gets reviewed.
Can it work across multiple wikis, like an internal Confluence and a customer-facing Zendesk help center?
Yes, as long as each platform exposes an API or export, both can be scanned on the same schedule with results reported separately by source.