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Case Studies

Real Results from Real Teams

See how engineering teams use Device Identification, Bot Detection, and Smart Signals to reduce fraud, block bots, and cut costs — with measurable results.

Company names anonymized at customer request.

FinanceGuard

Fintech · Series B

CASE STUDY #1

Challenge

$2.3M annual fraud losses from account takeover attacks. Existing WAF rules caught less than 40% of sophisticated ATO attempts, and manual review was not scaling.

Solution

Deployed 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.

78%
Fraud Reduction
$1.8M
Saved Annually
0
False Positives
2 weeks
Integration Time

We went from reviewing 200 fraud alerts per day to under 40. The false positive rate dropped to zero on legitimate users — our support team noticed before we did.

VP of Engineering, FinanceGuard

Integration timeline: 2 weeks to full integration

DataStack

SaaS · Enterprise

CASE STUDY #2

Challenge

Credential stuffing attacks averaging 50K+ attempts per day were bypassing their existing WAF. Attack traffic was consuming infrastructure resources and degrading performance for real users.

Solution

Bot Detection deployed at the edge to intercept automated traffic before it hits application servers. Smart Signals added for server-side session analysis to catch sophisticated bots that mimic human behavior.

99.2%
Bot Blocking Rate
40%
Infra Cost Reduction
50K+
Daily Bot Attempts
<5ms
Edge Latency Added

We were spending $18K/month on infrastructure just to serve bot traffic. Bot Detection cut that by 40% in the first week.

CTO, DataStack

Integration timeline: 3 days for Bot Detection, 1 week for Smart Signals

CloudMetrics

Analytics Platform · Growth

CASE STUDY #3

Challenge

Migrating from FingerprintJS Pro with concerns about accuracy regression during the transition. Needed to validate accuracy parity before committing to the switch.

Solution

Full migration to Device Identification with 30 days of parallel running against FingerprintJS Pro. Side-by-side comparison dashboard tracked accuracy, latency, and identification stability.

+2%
Higher Accuracy
45%
Cost Reduction
15ms
Faster Response
30 days
Parallel Run

We ran both systems in parallel for a full month. Device Identification matched or beat FingerprintJS Pro on every metric we tracked. The migration was a no-brainer.

Head of Data, CloudMetrics

Integration timeline: 30-day parallel run, then cutover

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