Call center ai · Production

CallRail improves call transcription accuracy by 23% and doubles conversation intelligence customers with AssemblyAI

The problem

Businesses needed faster, more efficient ways to extract and leverage insights to optimize customer acquisition, and CallRail needed a secure, scalable AI partner to build conversation intelligence features more quickly on top of the latest innovations.

Workflow diagram · grounded in source
1
Call ingested by platform
trigger
“when a call is run through CallRail's platform”
2
Speech-to-text transcription
ai_action
“CallRail first built a foundation of impressive features on top of AssemblyAI's speech-to-text and speech understanding models”
3
Call summarization
ai_action
“the summarization models synthesize lengthy audio and video data into highly relevant, actionable key points and takeaways. Results can be returned as several summary types, including bullets, bullets verbose, headline, gist, or paragraph”
4
Auto-score and categorize
ai_action
“the platform can auto-score and categorize key sections of the call to help CallRail's customers more intelligently and efficiently process call data at scale”
5
Sentiment analysis
ai_action
“sentiment analysis model, which can automatically detect and label sentiments—positive, neutral, or negative—in speech segments in its customers' conversational data”
6
LeMUR insight extraction
ai_action
“Additional Conversation Intelligence features are powered by AssemblyAI's LeMUR, which lets users leverage LLM capabilities to extract insights from voice data”
7
Follow-up action triggered
output
“automatically categorize calls, trigger a follow-up action, and more”
Reported outcome

Through its partnership with AssemblyAI, CallRail improved call transcription accuracy by up to 23% and doubled its conversation intelligence customer base.
Customers saw 50% less time reviewing calls, 60% less time qualifying leads, and a 10% increase in leads from improved marketing.

Reported metrics
Call transcription accuracy23%
Conversation intelligence customersdoubled
Time spent reviewing and analyzing calls50%
Time spent qualifying leads60%
Show all 5 reported metrics
call transcription accuracy23%
conversation intelligence customersdoubled
time spent reviewing and analyzing calls50%
time spent qualifying leads60%
leads from improved marketing10%
Reported stack
AssemblyAILeMURConversational Summarization Modelsentiment analysisspeech-to-textClaude LLMsAWS bedrock
Source
https://www.assemblyai.com/customers/callrail-customer-story
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Through its partnership with AssemblyAI, CallRail improved call transcription accuracy by up to 23% and doubled its conversation intelligence customer base.

What tools did this team use?

AssemblyAI, LeMUR, Conversational Summarization Model, sentiment analysis, speech-to-text, Claude LLMs, AWS bedrock.

What results were reported?

Call transcription accuracy: 23%; Conversation intelligence customers: doubled; Time spent reviewing and analyzing calls: 50%; Time spent qualifying leads: 60% (source-reported, not independently verified).

How is this call center ai AI workflow structured?

Call ingested by platform → Speech-to-text transcription → Call summarization → Auto-score and categorize → Sentiment analysis → LeMUR insight extraction → Follow-up action triggered.

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