AeroSynth™ – A GenAI-powered swarm of digital-twin agents that predicts flight disruptions up to 72 hours ahead, autonomously re-optimises the network, and sends policy-grounded, personalised messages to every affected Problem  
• Flight disruptions cost airlines ~8 % of revenue and erode customer trust.  
• Existing optimisation engines react after the fact and require manual rule updates.  
• Recent chatbot-liability incidents show AI outputs must be traceable to policy.

Solution – AeroSynth™  
1. Data Fabric  
   – Ingest live ADS-B, weather, maintenance, crew-roster, and CRM streams into a time-aware vector knowledge graph.  
2. Digital-Twin Core  
   – Every flight, crew, gate, and passenger becomes an LLM agent inside a 72-hour forward-looking graph.  
   – Agents negotiate swaps in real time; a reinforcement-learning orchestrator balances cost, CO₂, and customer experience.  
3. Passenger Co-Pilot  
   – Generates empathetic re-booking messages and vouchers in 50+ languages, each citing the exact tariff or union clause that justifies it.  
4. SDLC Co-Pilot  
   – Converts new operational rules into Jira epics, code stubs, unit + UI tests, and auto-creates pull requests—keeping the twin and production in lock-step.

Business Impact (first 12 months)  
• 25 % fewer delay minutes ⇒ ≈ CAD $12 M yearly fuel & crew-overtime savings.  
• +2 pt on-time-performance and +8 NPS for disrupted passengers.  
• Extra 10 % reduction in call-centre load by addressing customers proactively.

Implementation Roadmap  
P0 (6 wks): Pilot on YYZ–YUL corridor; baseline KPIs.  
P1 (3 mo): Deploy twin + swarm with human-in-loop approvals.  
P2 (9 mo): Scale to full network, activate SDLC Co-Pilot, RL cost-tuning.  
P3 (12 mo): Edge inference on wide-body aircraft; open API for alliance partners.

Tech Snapshot  
• 8 B-parameter open-source LLM fine-tuned on ops + policy corpus.  
• Ray for multi-agent execution, Milvus for vector DB, MLflow/Dataiku for MLOps, GitHub Actions/ArgoCD for DevOps.  
• TCS accelerators: Datom.ai (ingestion) and AI-Blueprint (governance).

Key Differentiators  
✔ Multi-agent reasoning at network scale with no single point of failure.  
✔ Self-documenting codebase—every optimisation rule ships with tests & docs.  
✔ Policy-grounded GenAI prevents the chatbot-liability issue from re-occurring.  
✔ Template can be re-used across other carriers, MROs, and rail operators—creating reusable IP for TCS.

Compliance  
All data remains within approved environments; outputs are fully traceable to source policy chunks, satisfying Canada’s pending AI & Data Act.