SpyGlow

SpyGlow is a competitive-intelligence product I founded and built to turn changes on public competitor websites into evidence-backed actions.

The product problem

Website monitoring is easy to describe and difficult to make trustworthy. Pages fail to load, consent screens appear, navigation changes and minor markup differences can look like meaningful business events. Sending every difference to an AI model produces noisy alerts and unnecessary cost.

The engineering approach

I designed the monitoring path as a layered pipeline. Capture-quality checks reject weak snapshots, suspicious results are rechecked, and deterministic comparison identifies real changes before AI analysis begins. Validated changes can then feed summaries, alerts, reports, AskGlow and evidence-linked battle cards.

The surrounding system coordinates scheduled monitoring, browser work, queues, notifications and operational recovery. Each layer has a clear responsibility, so AI adds judgment after the system has established reliable evidence.

Read the case study on reliable change intelligence.