Mon–Fri: 9:00 AM – 6:00 PM (UK)
All case studies
Thomson Reuters
News & Information

Cut editorial video-review time from hours to minutes, with an editor sign-off gate and full source traceability on every AI summary

We built a production pipeline that ingests broadcast video, transcribes it and produces editorially-formatted summaries, cutting analyst review time from hours to an estimated few minutes per video, with a mandatory editor sign-off gate and traceability from every summary back to the source footage and transcript.

The challenge

Editorial teams were spending hours manually reviewing long-form video to extract key moments, quotes and summaries for downstream products, a bottleneck at exactly the moments news breaks. In a newsroom, an ungrounded or misattributed AI summary is a reputational and accuracy risk, so a generic summarisation tool that could not cite its source or be reviewed before publication was unusable. The pipeline had to preserve editorial voice and keep a human in control of what ships.

What we built

1

Designed a serverless ingestion pipeline on AWS to handle large video files with chunked, fault-tolerant, observable processing

2

Combined Amazon Transcribe for speech-to-text with foundation models on Bedrock for editorial-style summarisation against a custom prompt-and-style guide

3

Made every summary traceable back to its transcript timestamp and source footage, so editors can verify any claim before it is used

4

Added a human review surface with an explicit sign-off gate, so editors accept, edit or reject AI output and feed corrections back into the pipeline

Architecture

Thomson Reuters architecture diagram

Outcomes

Cut analyst review time from hours to an estimated few minutes per video, with an editor sign-off gate and source traceability on every summary

Pipeline scales to peak news cycles without manual intervention and with full processing observability

Editorial voice preserved through prompt engineering plus a reviewer feedback loop

No AI summary reaches a downstream product without human sign-off

Results

38→4 min

analyst review time

2.8×

analyst throughput

100%

answers cited to source

£340k

annual hours reallocated

Stack
AWS Bedrock
Amazon Transcribe
AWS Lambda
Step Functions
S3
Ready when you are

Ready to take AI from pilot to production?

Book a free discovery call with our team of ex-AWS engineers. We'll discuss your regulated-industry challenges and outline a governed path from pilot to production. No commitment required.