Obrabot — internal voice AI analytics cabinet at Prof-IT
I run it as the PM: a personal cabinet with analytics and dashboards for the group's voice AI agents.
- Product
- Voice AI
- B2B
- Analytics
Obrabot: internal voice AI analytics cabinet
Obrabot is an internal product at the Prof-IT group. It’s a personal cabinet where customers and operators see what voice AI agents actually do — what they talk about, where the script breaks, and which calls need a human in the loop.
I run the product: shape the requirements, prioritize the backlog, align UX and data model with the team and engineers.
The class of problem
A voice AI agent without the right dashboard is a black box. The team can’t see:
- which segments of calls go off-script;
- where the AI agent performs worse than a human and why;
- which specific calls a person actually needs to listen to;
- how the conversation metric shifts from iteration to iteration.
Without this layer the product sells worse, because the customer doesn’t trust what they can’t verify.
What’s inside
- AI call segmentation. Calls are automatically tagged by outcome, refusals, tone, and unusual behavior.
- Dashboards by agent and script. Prompt version comparisons, scenario A/B, daily and per-campaign trends.
- Drill-down to a specific call. Transcript, tags, audio, metadata — all on one screen.
- Early signals. Surfacing dialogs where a human should step in before the customer complains.
Stack
Next.js, TypeScript, Prisma + PostgreSQL, OpenAI API, integrations with Prof-IT infrastructure.
What this case shows about me
I’m not an “external AI consultant” talking about voice AI’s potential. I run an actual product inside the Prof-IT group, where voice AI is the main product line — not a pilot. That means real operational pain, real customers, NDA on the numbers, and accountability for outcomes.
UI screenshots and metrics are under NDA — happy to discuss details in a private conversation.