Story
Hugging Face
InfobipInfobip Uses Inference Endpoints for Spoken Interfaces
Infobip, an information technology organization in Croatia, uses Inference Endpoints from Hugging Face to support customer and product conversations for product and contact center teams.
Infobip runs customer and product conversations on Hugging Face so ownership is obvious when work stalls.
“Information Technology work at Infobip spans more than one site, even when headquarters sits in Croatia.”
“Product and contact center teams asked for a shared way to run customer and product conversations without freezing local judgment.”
Independent write-up. Figures come from public sources or are illustrative.
Value results
| Category | Value result |
|---|---|
| Productivity | Fewer stalled items because spoken interfaces has a clear owner |
| Productivity | Handoffs in customer and product conversations sit in a shared queue instead of a mailbox trail |
| Capability | New joiners can see how customer and product conversations actually runs |
Story
Information Technology work at Infobip spans more than one site, even when headquarters sits in Croatia. Spoken interfaces was splitting across regional habits. Product and contact center teams asked for a shared way to run customer and product conversations without freezing local judgment.
Hugging Face (Inference Endpoints) is what they standardized on. Hugging Face is the open platform for machine learning models, datasets, and inference that teams use to share and serve models. Infobip uses it as the system of record for spoken interfaces, with product and contact center teams as the primary operators and other groups coming in through the same queue.
Leaders get a picture they can actually walk. Teams get fewer mystery statuses. The story is about operating change, not an unpublished percentage.
