Back office ops · Production

Grain increased customer satisfaction by 12% after integrating AssemblyAI

The problem

Grain needed the highest possible transcription accuracy to generate intelligent insights for its customers, which led the product team to switch from their previous provider, Rev, to AssemblyAI.

First attempt

Grain's previous transcription provider, Rev, did not deliver the accuracy level required to power high-quality AI insights.

Workflow diagram · grounded in source
1
Conversation captured as data
trigger
“every conversation—from phone calls to lectures to team meetings—can be considered data that can be put in a digital format and manipulated by Large Language Models, or LLMs”
2
Voice AI transcription
ai_action
“Grain's product team can ensure all transcripts produced by its platform are at the highest accuracy”
3
LLM analysis and summarization
ai_action
“leveraging Large Language Models (LLMs) to help our customers make better sense of this data through AI identification, flagging, highlighting, clipping, and summarization”
4
Intelligent insights delivered
output
“highly accurate transcripts translate into more intelligent insights for Grain's customers”
Reported outcome

After integrating AssemblyAI's Voice AI models, Grain saw customer satisfaction increase by 12%, and the platform can now accurately serve its highly international customer base in core languages.

Reported metrics
Customer satisfaction increase12%
Reported stack
AssemblyAILLMsAutomatic Language Detection (ALD)
Source
https://www.assemblyai.com/customers/grain-customer-story
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

After integrating AssemblyAI's Voice AI models, Grain saw customer satisfaction increase by 12%, and the platform can now accurately serve its highly international customer base in core languages.

What tools did this team use?

AssemblyAI, LLMs, Automatic Language Detection (ALD).

What results were reported?

Customer satisfaction increase: 12% (source-reported, not independently verified).

What failed first in this deployment?

Grain's previous transcription provider, Rev, did not deliver the accuracy level required to power high-quality AI insights.

How is this back office ops AI workflow structured?

Conversation captured as data → Voice AI transcription → LLM analysis and summarization → Intelligent insights delivered.

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