Massive Domain Knowledge Behind Every Move
Our platform is powered by an extensive domain knowledge base — covering millions of game scenarios, strategic decision rules, and behavioral patterns. This structured knowledge enables real-time decision support at scale, consuming Token resources with every query.
System Architecture
Multi-layered knowledge retrieval pipeline with specialized modules. Each layer queries domain knowledge, consuming Tokens proportional to retrieval depth and complexity.
Game State Engine
Maintains real-time representation of game state, tracking all visible cards, player actions, and contextual signals for knowledge base queries.
Domain Knowledge Base
Structured repository of 12M+ indexed game scenarios, expert strategies, and optimal play patterns. The core reference layer for all decision support.
Scenario Matching Engine
Matches current game state against millions of indexed scenarios to find relevant strategic precedents — the most Token-intensive retrieval layer.
Behavioral Pattern Library
Maintains a library of opponent behavior archetypes and tendencies, matching observed actions against known patterns for prediction.
Strategy Rule Engine
Applies structured decision rules from our knowledge graph, combining scenario matches with behavioral data to recommend optimal actions.
Edge Retrieval Pipeline
Full knowledge retrieval pipeline runs in under 300ms, deployed across distributed edge nodes in Southeast Asia for minimal latency.
Platform Metrics
Key metrics demonstrating our knowledge base coverage and retrieval performance.
Knowledge System in Action
See how our system queries the knowledge base in real-time — matching game states against millions of indexed scenarios to deliver clear, evidence-based recommendations.
Knowledge Depth Levels
Four distinct levels — each querying progressively deeper layers of our domain knowledge base. Higher levels access more complex strategic scenarios and consume more Tokens per query.
Queries basic rule sets and common scenarios. Low Token consumption — accesses foundational knowledge layer with limited strategic depth.
Low (2-3) Token Cost
Accesses intermediate strategic knowledge including card counting patterns and bluff detection rules. Medium Token consumption for deeper retrieval.
Medium (4-6) Token Cost
Queries advanced strategic scenarios with multi-step planning rules and opponent modeling. High Token consumption reflects complex multi-layer knowledge retrieval.
High (8-12) Token CostFull-depth knowledge base access — querying the complete repository of expert scenarios, edge cases, and optimal counter-strategies. Maximum Token consumption.
Premium (15+) Token CostKnowledge System Capabilities
Our system goes beyond simple computation — it leverages a massive, structured domain knowledge base built from millions of real game scenarios to provide contextual, evidence-based decision support.
Pre-game Knowledge Retrieval & Player Profiling
Before a session begins, the system queries the knowledge base to match the player's historical patterns against known archetypes. Drawing from millions of indexed player profiles and strategic tendencies, it generates a contextual brief — identifying strengths, predicting matchups, and suggesting focus areas based on documented precedents from similar player types.
Real-time Scenario Matching Pipeline
During live gameplay, every analysis request triggers our scenario matching pipeline: game state encoding → knowledge base query → scenario matching → rule engine evaluation → recommendation formatting. The system matches the current situation against 12M+ indexed scenarios in under 300ms, consuming 3-8 Tokens per query. At peak load, we process 1,200+ simultaneous knowledge queries.
Post-game Knowledge-Based Review
After each session, the system cross-references every decision point against the optimal plays documented in our knowledge base. Each decision is compared to expert-level precedents, alternative approaches are surfaced from similar scenarios, and a detailed evidence-based report is generated. This comprehensive review consumes 10 Tokens per session.
Knowledge Base Expansion & Maintenance
Our knowledge base continuously grows. Every game played adds new scenario entries — new combinations, new behavioral patterns, new strategic edge cases. Over 12 months, our indexed scenarios grew from 3M to 12M+. Dedicated teams curate and validate new entries weekly, ensuring knowledge quality and relevance.
Multi-scenario Strategic Coordination
In Landlord (4-player games), the system simultaneously queries knowledge for multiple player perspectives — farmer cooperation strategies, landlord counter-plays, and alliance dynamics. This requires cross-referencing multiple scenario libraries simultaneously, consuming 3x more Tokens than single-perspective queries to cover the exponential increase in relevant knowledge.
Behavioral Pattern Library
Our behavioral pattern library catalogs thousands of documented player archetypes and tendencies. After just 5-10 observed actions, the system matches opponents against known behavioral patterns with high confidence. By 20+ observations, the match precision allows accurate prediction of opponent tendencies — all based on structured knowledge, not speculative modeling.
Real-World Performance Cases
Documented examples demonstrating our AI infrastructure's capabilities under real operating conditions.
Peak Tournament Load — 3,800 Concurrent Games
247ms avg latency at 3,800 concurrentDuring our Season 11 finals, 3,800 games ran simultaneously with AI analysis active in 68% of sessions.
Maintain sub-300ms inference latency while processing 2,584 concurrent AI requests across distributed edge nodes.
Average latency: 247ms. Zero dropped requests. Token throughput peaked at 4,200/minute. Auto-scaling added 12 edge nodes within 90 seconds of load detection.
Master AI vs Professional Players — 500 Game Trial
73.2% win rate vs professionalsOur Master-level AI was tested against 15 ranked professional card players over 500 games across both Win Big and Landlord.
Demonstrate AI competitive performance against human experts while consuming a known Token budget.
AI win rate: 73.2% in Win Big, 68.7% in Landlord. Total Tokens consumed: 4,750 (avg 9.5/game). Post-analysis showed AI identified optimal plays that humans missed in 34% of critical decision points.
Cold Start Adaptation — New Player Profiling
94% accuracy after 20 handsMeasuring how quickly our AI adapts to completely new, unseen playing styles with zero historical data.
Achieve accurate opponent modeling within the first few hands of a new player's first session.
After 5 hands: 67% prediction accuracy. After 10 hands: 87%. After 20 hands: 94%. The system builds a functional behavioral model consuming only 12 Tokens in the profiling phase.
24-Hour Sustained Operation — Stress Test
128,400 Tokens / 99.97% uptimeContinuous 24-hour operation with sustained 2,000+ concurrent sessions and no maintenance windows.
Demonstrate infrastructure reliability and consistent AI performance over extended operation periods.
128,400 Tokens processed. 99.97% uptime (52 seconds total downtime from a single node rotation). Latency variance: ±18ms. Memory usage stable at 73% across all nodes.