How to Build a Test Data Search Engine with AI

Using AI agents to discover, compare and analyse test results

Every test rig and every programme captures more test data than the teams running them can use. So why does answering a single question still mean finding the file, decoding it, and building the analysis by hand?

This whitepaper sets out an architecture that lays a clean, connected data foundation first, then lets agentic AI work on top, so a plain-language question returns an answer instead of a week of manual work.

Read the whitepaper to understand:

• Why the constraint has moved from capturing data to finding, trusting, and acting on it
• A practical ladder of AI use in testing, and where most R&D organisations sit today
• The two-agent approach: one agent builds the data layer, one turns questions into results
• Just-in-time software, where the analysis is generated to fit the question
• What has to be true of the data foundation before agentic AI delivers anything trustworthy