Case study
DecafSupport AI turns an operational datasource that only specialists could use into a governed layer any authorized team can explore, query in plain language, and use for decisions.
Built inside Decaf by Decaf Labs. We tested the pattern on our own support operation first.
The problem
Support conversations, tickets and claims held the clearest picture of what customers were hitting. Reaching that picture meant knowing the source system, writing queries, or asking the one person who could. Most questions simply went unasked.
What we built
An intelligence layer that connects to the datasource, indexes and structures it under shared definitions, applies team permissions, and answers plain-language questions with the evidence linked.
Three issues account for most of this week's claim contacts:
Cashout delays are concentrated in two corridors and rose midweek.
"Cashout started Monday, still says processing. Client asking twice a day."conversation · anonymized
"Second KYC attempt rejected again, same document."claim note · anonymized
Representative interface · synthetic sample data
| Issue | Contacts | Status | Trend |
|---|---|---|---|
| Cashout delay | 41 | 18 open | ▲ rising |
| KYC retry loop | 27 | 6 open | ▼ falling |
| Payment link expired | 16 | 3 open | ▼ falling |
| Balance mismatch | 9 | 2 open | — flat |
Interface recreated with sample data
How it fits together
One source, different questions
What issues are generating repeat contacts?
Which workflows create the most confusion?
Where are cases becoming stuck?
What changed this week, and why?
The build
The result
DecafSupport AI is one specialized agent in Decaf's internal AI operating layer. The same architecture can turn support, sales, finance, compliance, or operational data into governed intelligence that every authorized team can use.
Your company already has valuable knowledge trapped in specialist systems. We can make it visible, queryable, and useful across the organization.