The coaching overlay appears only in the sales rep's browser window — the prospect never sees it. It functions like a private teleprompter that listens to the conversation and surfaces relevant guidance. When the system detects an objection pattern (e.g., 'pricing is too high'), it shows the relevant battle card or suggested response within 2 seconds. The rep can glance at it while continuing the conversation naturally.

The transcription stream feeds a continuously rolling context window into an LLM fine-tuned on sales conversation patterns. The model is configured with a library of objection patterns, competitor names, and buying signal phrases specific to your products and industry. New signals can be added via a configuration interface — no model retraining required for most additions.

CentEdge builds a fully on-premise deployment option where the transcription engine, LLM inference, and data storage all run within your own VPC or on bare-metal servers. No audio or transcript data is sent to external APIs. For cloud deployments, all data is encrypted at rest and in transit, with data residency controls ensuring no data crosses jurisdictional boundaries.

Native out-of-box integrations are available for Salesforce, HubSpot, and Zoho CRM. Post-call, a structured JSON payload with the call summary, speaker turns, action items, deal probability score, and next-step recommendations is pushed via the CRM's REST API. Custom field mapping and workflow trigger configuration are included in the build.

Managers see a team-wide dashboard showing average scorecard metrics per rep, trending weak areas across the team, and a call library filtered by score, product, or objection type. They can listen to flagged moments from any call with AI annotations already overlaid. Calibration workflows let managers agree on scores collaboratively before publishing to reps.