IT SERVICE MANAGEMENT
A conversational IT service desk in chat, live in seconds
Employees ask for help in chat; IT works in its service-management tool. Wexa built a service desk that answers from IT knowledge, escalates into real incidents, and turns a manual resolution into an automation, with no database, vector store or workflow engine of its own.
- self-service answer citing past incidents
- to open a real incident on escalation
- go-live: 2 connectors and 5 skills provisioned
- to hold a request containing personal data
- CLIENT
- Enterprise IT team on a leading workflow platform
- REGION
- US
- WHEN
- 2026
- ENGAGEMENT
- Solution build
- WEXA COMPONENTS
- Wexa · chat and ITSM connectors · knowledge base · process flows · approvals · data tables
01Case 11
The challenge
- Requests arrive in chat; knowledge lives in the ITSM tool
- Repetitive requests resolved by hand every time
- Personal data must never reach a model
What Wexa built
- Resolver: knowledge-base retrieval plus one model call → answer, automate, clarify or escalate
- "Automate this": a manual resolution compiled into a deterministic process flow that runs the next matching request
- Escalations open incidents; IT replies from the console into the chat thread; resolutions feed the knowledge base
- Personal data held for human approval; parked writes resume after approval, even across restarts
02Case 11
How it runs on Wexa
03Case 11
Results
| Measure | Before / without Wexa | With Wexa |
|---|---|---|
Service-desk agents for 2,000 requests a month | 12 agents | 5 agents |
Annual cost of the process | $840K | $350K |
Time to implement the AI solution | 6 months | 1 week |
AI build cost (one-time) | $480K | $8K |
AI operating cost per year (models, infrastructure, upkeep, platform) | $312K | $166K |
Total AI cost in year 1 | $792K | $174K |
With Wexa
net annual value to the client after Wexa's own operating cost
faster to implement
lower AI operating cost
less AI spend in year 1
- End-to-end proof run used 57 Wexa calls and nothing else
- Real people from the chat workspace and real incidents in the ITSM instance
More case studies
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−80%
tokens vs sending raw data to the model (up to −82%)
~80% fewer tokens and 18/20 correct vs 8/20 for a plain model call; zero permission leaks
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6 vs 24
inspections needed (graph cohort vs PLM-only)
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Read the caseConsumer packaged goods
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−72%
tokens per case vs the same agents without Wexa (59,269 → 16,559)
72–81% fewer tokens than the same agents without Wexa; 58% lower cost; actions written only after approval
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