Case patterns

What actually worked — from real iGaming AI systems.

Real engineering decisions, anonymized where required, honest about what didn't work. Each one tied to the commercial result it produced.

01

Bonus budget that stopped leaking

Result
Reactivation budget redirected away from players who'd have returned anyway. The CRM team finally stopped subsidising organic comebacks.
Mechanism
Causal uplift modeling: P(deposit | bonus) − P(deposit | no bonus) — not a churn classifier. We score who actually changes behaviour, not who looks risky.
Why it matters
Classification finds who's leaving. Only uplift finds who's worth paying. Most retention budgets in iGaming are still spent against a churn score, and the math doesn't work.

Origin: production retention system built inside a large European iGaming operator running six brands, 2024–2025.

02

RG alerts the team could actually action

Result
Alert volume reduced from ~200/day to ~25/day with more real cases caught, not fewer. Lower regulatory exposure, RG team no longer drowning.
Mechanism
LLM-based communication analysis fused with behavioral signals (deposit velocity, session pattern, chasing-loss pattern) and trend tracking. SGR keeps reasoning structured and auditable.
Why it matters
A compliance team drowning in false positives misses the real cases that cause fines. Quality of alert beats quantity, always.

Origin: production RG compliance system built inside a large European iGaming operator running six brands, 2024–2025.

03

Fraud rings the rule engine couldn't see

Result
Organised bonus-abuse rings caught — without touching player PII and without depending on fingerprinting.
Mechanism
Behavioural clustering on play patterns: bet rhythm, session shape, deposit cadence, withdrawal timing. VPN- and antidetect-resistant by design.
Why it matters
The abuse arms race moved. Rule-based promo protection now leaks at every threshold update. Behaviour stays harder to fake than browser fingerprints.

Origin: production fraud detection system built inside a large European iGaming operator running six brands, 2024–2025.

04

Support cost down without players noticing

Result
Routine L1 majority resolved by AI, human cost concentrated on VIP and complex cases. CSAT held or improved.
Mechanism
Multi-agent decomposition: triage, knowledge, action, escalation, QA — each agent narrow, predictable, auditable. Not a single mega-chatbot.
Why it matters
Single chatbots cap at 20–30% and route the rest to humans anyway — you pay twice. Decomposition is what makes the unit economics actually work.

Origin: 2026 European operator deployment.

05

QA that finally covered more than 2% of chats

Result
QA coverage went from 2% → 25% of conversations. Evaluator accuracy 66% → 91%. Both running on a cheap model.
Mechanism
Schema-Guided Reasoning (SGR) instead of free-form prompting. The model fills a structured schema before scoring — drastically reduces hallucination and lets cheap models score reliably.
Why it matters
2% manual QA coverage is statistically meaningless — you can't manage what you can't measure. SGR makes 25%+ coverage economically realistic.

Origin: production QA system built inside a large European iGaming operator running six brands, 2024–2025.

Want this result for your operation?

30 minutes, no pitch deck. We look at your retention spend, your churn pattern and your support cost, and tell you honestly which of these patterns would move the needle for you.

Or write: hello@arctura.eu