Illustrative Scenarios
What Device Intelligence Changes
How engineering teams put Device Identification, Bot Detection, and Smart Signals to work against fraud, bots, and infrastructure cost — walked through as concrete scenarios.
Illustrative scenarios modeled on typical deployments — not named customer engagements. Figures describe the kind of outcome these deployments target, not audited results. Real case studies will be published as customers approve them.
Example: Payments platform
Fintech · Series B profile
Challenge
A payments platform facing heavy account takeover losses, where WAF rules catch only a fraction of sophisticated ATO attempts and manual review does not scale with growth.
Solution
Deploy Device Identification and Bot Detection across login and payment flows. Device Identification flags returning devices even after cookie clearing, while Bot Detection intercepts automated credential testing at the edge.
What changes
In this scenario, the team moves from triaging hundreds of fraud alerts a day to a fraction of that volume, with far fewer legitimate users incorrectly flagged — the kind of shift a support team tends to notice first.
Example: Data infrastructure SaaS
SaaS · Enterprise profile
Challenge
A SaaS platform hit by large-scale credential-stuffing that bypasses its existing WAF. The attack traffic consumes infrastructure resources and degrades performance for real users.
Solution
Deploy Bot Detection at the edge to intercept automated traffic before it reaches application servers, and add Smart Signals for server-side session analysis that catches sophisticated bots mimicking human behavior.
What changes
In this scenario, a large share of the infrastructure spend that previously went to serving bot traffic is recovered, because most automated requests are turned away at the edge before they ever reach application servers.
Example: Analytics provider
Analytics Platform · Growth profile
Challenge
An analytics provider evaluating a migration away from another device-intelligence vendor, needing to validate accuracy parity before committing to the switch.
Solution
Run Device Identification in parallel with the incumbent for a validation period, using a side-by-side comparison dashboard that tracks accuracy, latency, and identification stability before cutover.
What changes
In this scenario, running both systems side by side lets the team confirm that Device Identification matches or improves on their previous vendor across the metrics they track, so the migration decision rests on their own data rather than a promise.
Ready to See Similar Results?
Talk to our team about how tracio.ai can reduce fraud and cut costs for your platform.