Case study / Revenue operations
Turning field notes into clean RepOps records
AI that turns case texts, surgeon preferences, and field-inventory notes into structured records, closing the gap between the ERP and the group chat.
- Client
- Covalt, medical-device RepOps
- Industry / size
- Medical device, rep operations
- Systems
- ERP, Field messaging, Inventory notes
Context
In medical-device rep operations, the real detail lives in the field: case texts, surgeon preferences, and inventory notes that move through messaging and group chats. The ERP is supposed to be the system of record, but the field rarely speaks its language.
The problem
That field detail had to be re-entered into the ERP by hand. It was slow, it was easy to get wrong, and the gap between the group chat and the system of record meant ops was constantly reconciling what actually happened against what was recorded.
What we built
We built AI that reads the field communication and turns case texts, surgeon preferences, and inventory notes into structured records the ERP can use. It closes the gap between the group chat and the system of record, so the detail captured in the field lands cleanly where the business needs it.
The result
Around eight hours a week of rep and ops admin came back, and the reconciliation work between the chat and the ERP shrank. Reps spend less time re-typing the field, and ops works from records that match what actually happened.
Results
Field notes to ERP
Re-typed by hand Structured automatically
Impact
Reps spend less time re-typing the field and more time in it.
- Around eight hours a week of rep and ops admin recovered
- Fewer gaps between what happens in the field and what the ERP knows
- Case texts, surgeon preferences, and inventory notes captured as structured records
How we estimate this: Value is the rep and ops admin hours recovered each week, plus fewer downstream errors from re-keying field data.
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