PEC Central · Live-Agent AI Copilot

The right guidance, inside every patient call.

Give every agent day-one institutional intelligence to improve first-call resolution, reduce handle time, and answer patients more consistently.

Explore PEC Central
Live-Agent AI Copilot

Give every agent the knowledge and guidance to resolve more calls.

Powered by Sherlock EKOL, PEC Central gives healthcare call center agents real-time, context-aware knowledge and workflow guidance—equipping new and tenured agents with day-one institutional intelligence.

Operational value

Built to improve the work behind every patient interaction.

01 · Resolution

Improve first-call resolution

Surface relevant guidance and next-best actions during the interaction so agents can resolve more needs without unnecessary transfers or callbacks.

02 · Efficiency

Reduce handle time and cognitive load

Bring workflow context into the live-agent experience instead of asking employees to search across disconnected systems and documents.

03 · Workforce

Deliver day-one agent intelligence

Equip new and tenured agents with the same current institutional knowledge as top performers, supporting faster onboarding and fewer escalations.

How it works

Understand the call. Surface the right guidance. Support resolution.

1

Capture interaction context

PEC Central keeps the patient need and available context visible inside the live-agent workflow.

2

Guide the next action

Approved knowledge, workflow guidance, and next-best actions are surfaced when the agent needs them.

3

Resolve and improve

The agent completes the interaction with greater consistency, while interaction data can inform quality and workflow improvement.

Connected by design

One product. One connected PEC360 platform.

Guided by
Sherlock EKOL provides governed enterprise knowledge and workflow guidance for the live-agent experience.
Receives from
AVA can collect and validate information before transferring a complex interaction to a live agent with context intact.
Measured by
Insights analyzes interaction quality, patient sentiment, agent performance, and recurring friction.