AI media-intelligence pipeline for law firms
Built a pipeline that analyzes social-media video for sentiment, content, and faces — turning raw footage into structured evidence signals.
// CONTEXT
A legal analytics client needed to extract evidence-grade signals from large volumes of social-media video — transcription, sentiment, content classification, and face detection — at a throughput no manual team could match.
// PROBLEM
The pipeline had to fan out across AWS media services, stay resilient to per-asset failures, and produce structured, queryable output that held up under legal scrutiny.
// ARCHITECTURE
ILLUSTRATIVE TOPOLOGY — NOT A CLIENT SYSTEM DIAGRAM
// DECISIONS
Chose Fan-out via SQS + Lambda over a monolithic worker
Each media service runs as an independent stage, so one slow or failing signal never blocks the others.
Chose Go for the orchestrator over the existing PHP stack
Concurrency and predictable memory made Go the right tool for coordinating thousands of parallel media jobs.
// OUTCOME
Eight thousand videos a month are processed into three structured signals each, with a median turnaround under a minute.
- 3
- signals per asset
- 8,000
- videos / month
- 52s
- median turnaround