Global media intelligence company cuts analysis time to 15 seconds

A global media SaaS company that analyzes hundreds of millions of daily news, social and broadcast sources needed to speed up crisis-signal detection and competitor narrative tracking, which previously took analysts hours to synthesize. Snorkel built a multi-agent conversational intelligence system with a custom evaluation harness grounded in the client's own definitions of decision-grade, safe answers. After upgrading to GPT-5.4-mini and re-testing against that harness, average response time fell to 15.2 seconds, the safety/governance score rose from 82.6% to 98.6%, and decision usefulness improved by 5 points with a 100% pass rate on safety-critical refusal checks. The evaluation-first approach lets the client swap or test new models without rebuilding the agent.

The customer is anonymised in Snorkel AI’s own case study — not withheld by us.

Snorkel AI is a data-development and evaluation company whose programmatic labeling tools build training and benchmark datasets used to stress-test whether a model approach is viable before a full build.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.