Back office ops · Production

Earmark achieves 83% cost reduction and unlimited scalability with AssemblyAI

The problem

Earmark needed a speech-to-text solution capable of unlimited concurrent streaming with sustainable economics and enterprise-grade privacy, as real-time transcription was the foundation of its entire PM platform.

Workflow diagram · grounded in source
1
Live PM meeting begins
trigger
“Its platform promises to transform live meeting conversations into finished deliverables, executing traditional PM work like briefs and documentation concurrently during conversations”
2
Real-time speech transcription
ai_action
“AssemblyAI transcribed every word in real-time while Earmark's AI agents structured and formatted the conversation”
3
AI agents structure content
ai_action
“Earmark's AI agents structured and formatted the conversation into comprehensive notes—making it easy to transform their success story into this narrative”
4
Deliverables ready at meeting end
output
“when a PM finishes a stakeholder meeting, Earmark has already drafted the PRD, updated the roadmap documentation, and prepared follow-up action items”
Reported outcome

Earmark achieved an 83% reduction in streaming costs, the ability to handle 100+ concurrent streams without performance degradation, and a 4-day implementation, enabling sustainable unit economics and worry-free scaling.

Reported metrics
Streaming cost reduction83%
Streaming cost per hour$0.15 per hour
Monthly cost savingssaving thousands of dollars monthly
Concurrent streams supported100+
Show all 8 reported metrics
streaming cost reduction83%
streaming cost per hour$0.15 per hour
monthly cost savingssaving thousands of dollars monthly
concurrent streams supported100+
implementation time4 days
migration timeunder a week
unit economicssustainable unit economics enabling profitability
after-hours PM work eliminatedeliminating after-hours work
Reported stack
AssemblyAI
Source
https://www.assemblyai.com/customers/earmark-customer-story
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Earmark achieved an 83% reduction in streaming costs, the ability to handle 100+ concurrent streams without performance degradation, and a 4-day implementation, enabling sustainable unit economics and worry-free scaling.

What tools did this team use?

AssemblyAI.

What results were reported?

Streaming cost reduction: 83%; Streaming cost per hour: $0.15 per hour; Monthly cost savings: saving thousands of dollars monthly; Concurrent streams supported: 100+ (source-reported, not independently verified).

How is this back office ops AI workflow structured?

Live PM meeting begins → Real-time speech transcription → AI agents structure content → Deliverables ready at meeting end.