How do you turn the same repeat ticket into a KB article?
A support AI Employee connected to your help desk and your docs repo. Every week it clusters resolved tickets for questions that keep recurring with no help center article, drafts the top gaps from the actual resolutions, and opens a single pull request for a named human to review and publish.
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Every support team answers the same question more than once before anyone writes it down. A ticket gets resolved, the customer is happy, and the answer disappears back into the queue instead of becoming a help article. Weeks later the same question comes in again, gets solved the same way, and disappears again. The knowledge was generated a dozen times over and captured exactly zero. The material already exists, sitting in hundreds of resolved threads, but nobody has the time to mine it. Doing it by hand means a support lead has to remember which topics are already covered, judge which recurring questions are worth an article versus a one off, and then write each draft from scratch. That review happens rarely, if ever, and by the time it does the backlog is large enough that just picking where to start is its own project. A generic AI writer pointed at 'write some help articles' doesn't fix it either: with no memory of what's already covered and nothing grounding its drafts in how the team actually resolves the issue, it either duplicates existing articles or writes generic copy that matches no real resolution.
Your help desk is connected to an AI Employee that runs on a weekly schedule and resumes the same working memory each time rather than starting cold. Each run it reads recently resolved threads, clusters them into recurring topics, checks each topic against both the existing articles in your docs repo and its own record of what it has already drafted, and picks the top gaps: recurring questions with real ticket volume and no article. It drafts each one from the actual resolutions used in those tickets, then opens a single pull request. Nothing publishes without a human merge.
See exactly how the work gets done.
Runs weekly against its own record
A weekly schedule resumes the same working memory rather than starting fresh. It reads its own record first: which topics already have an article, which are mid review in an open pull request, and which clusters were seen before but didn't yet have the volume to justify a draft. That way nothing already covered or already in flight gets redrafted.
Carries your clustering and drafting rules
How tickets group into a topic, how a real KB gap is told apart from noise, and the article structure you want live as skills and memory that travel with the AI Employee. When a draft merges as is versus gets reworked heavily in review, that feeds back so the next batch lands closer to publishable.
Connects the ticket source and the docs repo, with permissions you set
It reads resolved tickets from the help desk read only, and reads the docs repo to see which topics already have an article, writing only as a pull request on an isolated branch via a scoped GitHub token. Credentials stay in memory, never written to disk, never exposed to the model.
Drafts only; never publishes
The AI Employee can open a pull request against an isolated branch and nothing more. It never pushes to the live docs branch and never merges its own work. A person reviews the drafts, edits them, and decides what ships to the help center.
Opens one pull request of drafts
Each week brings a single pull request: a small batch of article drafts, each built from the real resolutions of the tickets that raised the question, with ticket volume and topic history noted for the reviewer. The lead edits what needs editing and merges what's ready. The record advances either way, so next week starts from what's still uncovered.
Runs in your environment
Every run resumes inside your own infrastructure; your data never leaves it. The run can reach only the help desk and the docs repo, and only the pull request leaves.
Permissions you set
The help desk key and the GitHub token stay in memory, injected at run time, never written to disk, never exposed to the model or the logs.
Draft only
The AI Employee opens a pull request against an isolated branch and stops. It never pushes to the live docs branch and never merges its own work. A named human reviews and publishes.
Read only tickets
Help desk access is read only. The AI Employee cannot edit, close, or reply to a ticket while mining it for KB content.
You own it
The clustering rules, drafting standard, and per system permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
The backlog of 'we keep answering this but never wrote it down' questions now arrives as a small, reviewable pull request each week, every draft traceable back to the real tickets that produced it. The AI Employee only clusters and drafts; the support team decides what gets published.
Every week
Resolved tickets reclustered against the current KB gap set
Draft only
Every article ships as a pull request a human reviews and merges
1 record
Tracks which topics are covered so nothing is redrafted
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
How do AI Employees resolve support tickets before a human steps in?
Triages, investigates across product, code, and billing, and resolves what it can, escalating sensitive actions with the diagnosis already attached.
×How do you catch the support ticket about to breach SLA?
Scans the open queue every 15 minutes against SLA, VIP status, and severity, routes qualifying tickets to the right team, opens linked issues for engineering, and alerts an escalation channel, closing nothing and messaging no one.
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How do you keep a shared inbox triaged as mail lands?
Reads, labels, and either drafts a reply from the knowledge base or files a task. Customer facing replies land as drafts, held for a named human to send.
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