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Call center AI · Agent coaching

Conversation intelligence for sales and support agents.

A call intelligence platform for summarization, agent assist, SLA tracking, and coaching across sales and support conversations.

[ Client review ]

CoversaIQ became easier to explain as the system that turns every call into coaching, follow-up, and management visibility.

— Product team
CoversaIQ source visual showing call analytics on a laptop dashboard
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Client

CoversaIQ

Call center AI · Agent coaching

Engagement

Product narrative

Positioning · workflow story · product proof

Role

AI builder

Speech analytics product story

Year

2026

Project positioning

Buyer caseagent coaching outcomes
LLM
Agent assist

Guidance during and after calls

Speech
Transcription layer

Calls become searchable records

SLA
Service tracking

Follow-up and resolution visibility

Coach
Team improvement

Patterns surfaced for managers

[ 01 ]The Problem

Call center managers need more than transcripts after the fact.

CoversaIQ had to explain how transcription, LLM summaries, agent assist, and SLA tracking work together without sounding like separate features.

The buyer story needed to connect call quality, conversion, follow-up, and coaching into one operational loop.

[ 02 ]Why This Was Hard

Audio, transcripts, CRM notes, and manager reviews often live in separate tools.

Guidance needs to be timely, relevant, and reviewable.

The product had to show team-level coaching value, not only call summaries.

Follow-up and service commitments need reliable handoffs.

[ 03 ]Approach

We positioned CoversaIQ as the conversation layer between live calls and team improvement.

The workflow starts with call capture, extracts summaries and risks, assists agents, and gives managers coaching signals.

That made the product concrete for teams trying to cut handle time while improving conversion and service quality.

  • Automated transcription for support and sales calls.
  • LLM summaries, agent assist, and next-step extraction.
  • SLA tracking and handoff visibility for managers.
  • Coaching signals that turn call history into team improvement.
[ 04 ]Key Decisions

Call intelligence frame

The product was positioned as an operational layer for calls.

Speech foundation

Transcription made every downstream summary and signal inspectable.

Manager visibility

SLA tracking and coaching became part of the same story.

Agent assist

Real-time and post-call guidance were tied to next actions.

CRM context

Customer history made summaries more useful for follow-up.

[ 05 ]How We Shipped
Week 1-2

Workflow audit

Mapped sales and support call review paths.

Week 2-3

Narrative

Defined the call intelligence positioning.

Week 3-4

Signal map

Connected transcription, summaries, SLA rules, and coaching outputs.

Week 4-5

Manager story

Clarified how call records become team improvement signals.

Week 5-6

Launch copy

Turned the platform into concise buyer language.

[ 06 ]Value Profile
Handle-time claritySummaries and next steps reduce review drag.
84
Conversion supportAgents get guidance across sales conversations.
80
SLA visibilityService commitments become easier to track.
86
Manager insightCoaching patterns surface from call records.
82
[ 07 ]How It Works
[ 01 ] Sources
Call inputs
  • Audio
  • Transcript
  • CRM context
  • SLA rules
[ 02 ] Parse
Speech layer
  • Transcription
  • Speaker turns
  • Intent
  • Entities
[ 03 ] Assist
LLM coaching
  • Summaries
  • Agent assist
  • Risks
  • Next steps
[ 04 ] Deliver
Manager view
  • SLA tracking
  • Coaching
  • Reports
  • CRM sync

CoversaIQ joins transcription and LLM reasoning to the management workflow. The product is valuable when every call becomes a record, a summary, and a coaching signal.

[ 08 ]Outcome

Clearer platform story: CoversaIQ now reads as a call intelligence layer for sales and support teams.

Sharper operational promise: transcription, agent assist, SLA tracking, and coaching connect to one workflow.

"

CoversaIQ became easier to explain as the system that turns every call into coaching, follow-up, and management visibility.

P
Product team
[ 09 ]Stack
Sources
  • Call audio
  • Transcripts
  • CRM context
  • SLA rules
Processing
  • Speech-to-text
  • Speaker diarization
  • Intent extraction
  • Entity detection
Answer layer
  • Summaries
  • Agent assist
  • Coaching signals
  • Next steps
Delivery
  • Manager dashboard
  • CRM sync
  • SLA alerts
  • Reports
Governance
  • Review history
  • Call consent
  • Access control
  • Quality checks

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