WhatConverts
Call transcription
Transcripts lacked precision for ranking leads. AI now separates speakers and automatically scrubs credit card data from audio.
- Up to 10% improvement in transcription accuracy
Seven-hour meetings in thousands of jurisdictions: transcripts showed what came up—never who opposed it. Now every statement is attributed.
A platform indexing city council, planning commission, and school board meetings across all 50 states and DC, turning decisions that shape communities for decades into searchable civic data for contractors, site selectors, and government affairs teams.
Audio quality, accents, and speaking styles vary unpredictably across thousands of jurisdictions, and meetings routinely run five to seven hours with...
“The decisions made at the local level are very, very opaque and incredibly impactful to our day to day lives.”
Civic intelligence platform for searching and analyzing government meeting transcripts.
AI models for speech-to-text transcription and audio intelligence.
Hamlet's Meeting transcription is part of this use case:
Related implementations across industries and use cases
Transcripts lacked precision for ranking leads. AI now separates speakers and automatically scrubs credit card data from audio.
Space static broke standard tools, requiring manual logs. Custom models now run a real-time check and make archives searchable.
Inconsistent manual call notes caused billing errors. Now, zero-retention AI turns VoIP calls into synced case and billing entries.
Transcripts lacked precision for ranking leads. AI now separates speakers and automatically scrubs credit card data from audio.
Space static broke standard tools, requiring manual logs. Custom models now run a real-time check and make archives searchable.
Task-by-task, manual mapping couldn't keep pace with national recovery targets. AI now maps peatlands and tracks restoration across England.
600+ daily calls, 30-min waits, 70% abandoned. CORA now resolves half autonomously—staff handle what only humans can.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Seven-hour meetings in thousands of jurisdictions: transcripts showed what came up—never who opposed it. Now every statement is attributed.
A platform indexing city council, planning commission, and school board meetings across all 50 states and DC, turning decisions that shape communities for decades into searchable civic data for contractors, site selectors, and government affairs teams.
Audio quality, accents, and speaking styles vary unpredictably across thousands of jurisdictions, and meetings routinely run five to seven hours with...
“The decisions made at the local level are very, very opaque and incredibly impactful to our day to day lives.”
Civic intelligence platform for searching and analyzing government meeting transcripts.
AI models for speech-to-text transcription and audio intelligence.
Hamlet's Meeting transcription is part of this use case:
Related implementations across industries and use cases
Transcripts lacked precision for ranking leads. AI now separates speakers and automatically scrubs credit card data from audio.
Space static broke standard tools, requiring manual logs. Custom models now run a real-time check and make archives searchable.
Inconsistent manual call notes caused billing errors. Now, zero-retention AI turns VoIP calls into synced case and billing entries.
Transcripts lacked precision for ranking leads. AI now separates speakers and automatically scrubs credit card data from audio.
Space static broke standard tools, requiring manual logs. Custom models now run a real-time check and make archives searchable.
Task-by-task, manual mapping couldn't keep pace with national recovery targets. AI now maps peatlands and tracks restoration across England.
600+ daily calls, 30-min waits, 70% abandoned. CORA now resolves half autonomously—staff handle what only humans can.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.