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d6dae5bf - feat(spec): SPEC-WFAIPRO-001 — WorkflowAI Pro Technical Specification

feat(spec): SPEC-WFAIPRO-001 — WorkflowAI Pro Technical Specification Implementation-ready XML specification for WorkflowAI Pro enterprise workflow optimisation platform. Tri-model AI architecture: GNN document routing, collaborative filtering bottleneck prediction, active learning UI adaptation. Document: SPEC-WFAIPRO-001 v1.0.0 (52,955 bytes, 1,323 lines) Format: XML with CDATA-wrapped Markdown content 6 Required Sections — All Present: 1. Executive Summary — tri-model architecture overview, key differentiators table 2. System Architecture — syntax-valid Mermaid.js C4 Container diagram (13 containers, 3 external systems, 27 relationships) 3. AI Components — HeteroGAT GNN (18M params, <200ms P99), NCF with temporal attention (72h lookahead, >91% precision), pool-based AL with BatchBALD (200 labels/day, MC Dropout T=20) 4. Implementation Specs — Deep dive on 3 entities: - Document Router: OpenAPI 3.0 (3 endpoints), PostgreSQL schema (4 tables, RLS multi-tenancy, hash partitioning), Kafka (4 topics, exactly-once) - Approval Predictor: OpenAPI 3.0 (2 endpoints), MongoDB schema (2 collections with JSON Schema validation), Redis feature store (4 key patterns, TTL policy), Kafka (3 topics, 5-tier retry backoff) - Adaptive UI Engine: OpenAPI 3.0 (3 endpoints), MongoDB schema (2 collections: al_pool, al_experiments), Kafka (4 topics including model.retrained) 5. Performance, Security & Compliance — exactly 3 bullet points: SLAs, GDPR/SOC 2, RBAC (6 roles, 23 permissions, OPA enforcement) 6. 18-Month Roadmap & Risks — exactly 8 bullet points: Q1-Q6 milestones + 2 risk mitigations (model drift, Kafka backpressure) Validation: XML well-formed (Python ET parse), all section/content checks pass.