Context:
In the final phase of my work for Luckbox, I was appointed to define and lead product vision for an AI ML-driven, real-time micro-betting app integrated with live esports streams.
The system leveraged probabilistic models trained on historical game data from thousands of games to dynamically generate betting markets as matches unfolded in real time.
This was not a traditional sportsbook - it was an AI-driven decision system, continuously producing time sensitive probabilistic outputs.
The core challenge was designing the interaction architecture that translated high-frequency model predictions into cognitively accessible human decisions.
The underlying ML system was capable of producing complex betting markets based on evolving in-game states.
Left unconstrained, this would result in:
• Scenarios with multi-variable outcomes
• Rapidly shifting odds and distributions
• Long, dynamically generated market names
• High cognitive load under extreme time pressure
The model operated at system speed.
Player cognition did not.
Without deliberate constraint, the product would overwhelm users, and the interface itself would become unusable under dynamic output conditions.
My responsibility was to define the product architecture that shaped model output into a usable decision system.
Every market was framed as a question with a binary outcome: Yes / No or Team A / Team B.
This decision framework:
• Standardised model output formatting
• Minimised comparing odds under time pressure
• Reduced branching cognitive paths
• Preserved UI stability within fixed interface constraints
Stake values were pre-configured at session start, removing the need for in-play risk recalculation.
Markets were presented with a visible "burning wick" countdown mechanic to reinforce their ephemeral nature. If a user did not act within the window, the opportunity disappeared.
The result was a continuous stream of simplified, time-bound decisions which aligning machine-generated predictions with user decision making limits.
Beyond individual markets, I defined the broader system architecture:
• Lobby → Rapid In-Play Decisions → Immediate Resolution → Post-Game Summary → Return
• The lobby anchored stake expectations and session framing.
• In-play interactions prioritised speed and clarity.
• Resolution screens delivered immediate outcome feedback to maintain pacing.
• Post-game summaries closed the emotional loop and prepared users for re-entry.
The objective was not feature density, but behavioural coherence and decision velocity.
While ML specialists focused on model development and telemetry ingestion, I owned the end-to-end product vision and UX architecture, ensuring that probabilistic outputs were formatted, constrained, and delivered in ways that were cognitively sustainable and commercially viable.
As a result of the cessation of the Luckbox brand, the finished product was never
