Turning call recordings into insight.

Overview

Call recordings are full of signal — what customers asked for, how they felt, where conversations went sideways — but it's locked inside audio nobody has time to sit and listen to. This pipeline unlocks it: every call is automatically transcribed and analysed, turning hours of recordings into searchable text and metrics.

The problem

The recordings lived in one place and the speech-analytics engine lived in another — a separate AWS account entirely. Moving audio between the two had to be reliable, secure, and unbothered by volume. Speech analytics is only worth anything if every call actually makes it through; a pipeline that silently drops one call in fifty is worse than no pipeline at all.

What we built

An event-driven pipeline on AWS Lambda. As recordings land in S3, the pipeline picks them up, handles the cross-account transfer into the analytics environment, and submits each one to the speech-analytics engine for processing.

• Transcription of every call into searchable text
• Sentiment analysis across the course of each conversation
• Conversation and topic analysis to surface what calls were actually about
• A Lambda-based flow with cross-account S3 transfer, built to keep pace with call volume

The sharp edges

Cross-account S3 transfer is fiddlier than it looks — bucket policies, object ownership after a copy, and making sure a failed transfer retries instead of quietly disappearing. We built for idempotency and visibility: re-processing a call is always safe, and it's always clear which recordings made it through and which need another pass.

The result

Conversations that used to vanish the moment they ended now leave a trail — transcribed, scored and searchable — ready for quality assurance, trend-spotting, and the kind of analysis you simply can't do by hand across thousands of calls.