GiG Integrates VAIX Deep Learning for AI-Powered Personalisation Across Its Operator Platform

GiG Embeds VAIX AI Across Platform to Strengthen Operator Value Proposition

Gaming Innovation Group (GiG) has integrated VAIX's deep learning personalisation platform across its B2B iGaming technology stack, adding AI-native player engagement tools to its operator offering amid ongoing commercial pressure.

What Happened

GiG announced the VAIX partnership on April 1, 2026. VAIX — acquired by Sportradar in 2021 — provides deep learning models trained on iGaming-specific player behaviour data. The integration adds four capability layers to GiG's platform:

- Sports personalisation: AI-driven recommendations for events, leagues, markets, and BetBuilder combinations tailored to each player's historical betting behaviour - Casino personalisation: Netflix-style game carousel recommendations based on session history, preferred mechanics, and win/loss patterns - Cross-vertical personalisation: A casino content recommender specifically for sports-first players, designed to drive cross-sell - CRM and retention: Churn prediction models with customer lifetime value scoring, enabling operators to trigger targeted retention interventions before player disengagement occurs

VAIX cites performance benchmarks from existing deployments: +126% email click rates versus non-personalised campaigns and up to 37% churn reduction in markets where the full personalisation stack is active.

Why It Matters

GiG entered 2026 under financial pressure, having issued a profit warning in Q1 after a major anticipated Brazil contract failed to materialise. The VAIX integration serves a dual commercial purpose: it immediately upgrades the value of GiG's platform for its existing operator client base (increasing stickiness and reducing churn risk on the B2B side), and it strengthens the pitch for new operator acquisitions by adding a demonstrably high-ROI AI layer that smaller operators cannot easily build in-house.

Industry Context

The Sportradar/VAIX backing gives the AI tools enterprise-level credibility — these are not prototype recommendation engines but battle-tested models deployed across Sportradar's global operator network. For GiG's operator clients, the integration provides immediate access to personalisation capabilities that would typically require a dedicated data science team and 12–18 months of model training to build independently.