Your Team Already Wrote the Manual
The real way your business runs is scattered across chats, wikis, and ticket history. The agent reads all of it and turns it into a knowledge index where every answer cites its source.
Team chats
Years of support, data entry, and internal question channels in Teams or Slack. Every fix, exception, and "how do we handle this?" your people already answered.
SOP wikis and training
Notion, SharePoint, and OneDrive procedures, onboarding curricula, and checklists, with their dates, so outdated steps are flagged instead of followed.
Tickets and records
Your ticketing system, CRM, and database history: how requests were actually filed, assigned, and closed, not just how the manual says they should be.
Shared inboxes
The support and intake mailboxes where work arrives, read-only, with attachments and thread history kept as evidence.
Learn, Shadow, Score, Graduate
Learn
We ingest your operating history into a cited knowledge index and step-by-step process cards. Every rule links back to the message, page, or record it came from. Conflicting procedures become open questions, never guesses.
Shadow
The agent works every incoming request silently. It researches, drafts the full action plan, and seals its prediction before your team finishes the real work. Nothing it does touches a live system.
Score
A separate observer compares each sealed prediction with what your staff actually did. Disagreements go to a review queue with the original request, the governing rule, and both outcomes side by side.
Graduate
Workflows go live one at a time, only after the scorecard shows the agent gets them right. The rest stay in shadow until they earn it.
✕Typical AI rollout
- Trained on a generic prompt and a few FAQ pages
- Switched on in production on day one
- Mistakes found by customers, not by you
- Accuracy is a vendor's claim, not your measurement
- Old procedures followed because nobody flagged them
✓Shadow mode first
- Trained on your own chats, SOPs, and records, with citations
- Read-only until it proves itself, workflow by workflow
- Every prediction sealed before the real outcome is known
- Scored on your actual cases, including the hard ones
- Conflicting rules surface as questions for your team
What We Measure
Not one blended accuracy number. A scorecard per workflow, counting every eligible case, including the late, failed, and unmatched ones.
- ●End-to-end cases completed correctly, not just easy matches
- ●Customer, location, and contact linkage accuracy
- ●Required fields filled correctly and attachments kept
- ●Duplicate and no-action decisions, precision and recall
- ●Valid assignment against the real roster and rotation
- ●Unsafe or unsupported actions, invented facts, missed exceptions
- ●Time to a useful proposal, cost per case, and edits staff needed
A US Telecom Operator's Support Desk
What it learned
Years of support and data entry team chats, an internal Q&A channel, a large Notion knowledge base, and Microsoft 365 training files, distilled into dozens of cited process cards.
What it does
Reads each support email, matches the customer and location, checks for duplicates, drafts the ticket, and proposes the right assignee, all before staff finish the same case.
Where it stands
In shadow mode, building its scorecard. Intake goes live when the numbers say so, then the same approach extends to the rest of support.
AI Operations Agents: FAQ
Prove it first. Then let it work.
Tell us which workflow eats your team's week. We will show you what the agent would have done on your real cases before it does anything at all.