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| 1 | + | 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 | |
| 2 | + | • Flight disruptions cost airlines ~8 % of revenue and erode customer trust. | |
| 3 | + | • Existing optimisation engines react after the fact and require manual rule updates. | |
| 4 | + | • Recent chatbot-liability incidents show AI outputs must be traceable to policy. | |
| 5 | + | ||
| 6 | + | Solution – AeroSynth™ | |
| 7 | + | 1. Data Fabric | |
| 8 | + | – Ingest live ADS-B, weather, maintenance, crew-roster, and CRM streams into a time-aware vector knowledge graph. | |
| 9 | + | 2. Digital-Twin Core | |
| 10 | + | – Every flight, crew, gate, and passenger becomes an LLM agent inside a 72-hour forward-looking graph. | |
| 11 | + | – Agents negotiate swaps in real time; a reinforcement-learning orchestrator balances cost, CO₂, and customer experience. | |
| 12 | + | 3. Passenger Co-Pilot | |
| 13 | + | – Generates empathetic re-booking messages and vouchers in 50+ languages, each citing the exact tariff or union clause that justifies it. | |
| 14 | + | 4. SDLC Co-Pilot | |
| 15 | + | – 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. | |
| 16 | + | ||
| 17 | + | Business Impact (first 12 months) | |
| 18 | + | • 25 % fewer delay minutes ⇒ ≈ CAD $12 M yearly fuel & crew-overtime savings. | |
| 19 | + | • +2 pt on-time-performance and +8 NPS for disrupted passengers. | |
| 20 | + | • Extra 10 % reduction in call-centre load by addressing customers proactively. | |
| 21 | + | ||
| 22 | + | Implementation Roadmap | |
| 23 | + | P0 (6 wks): Pilot on YYZ–YUL corridor; baseline KPIs. | |
| 24 | + | P1 (3 mo): Deploy twin + swarm with human-in-loop approvals. | |
| 25 | + | P2 (9 mo): Scale to full network, activate SDLC Co-Pilot, RL cost-tuning. | |
| 26 | + | P3 (12 mo): Edge inference on wide-body aircraft; open API for alliance partners. | |
| 27 | + | ||
| 28 | + | Tech Snapshot | |
| 29 | + | • 8 B-parameter open-source LLM fine-tuned on ops + policy corpus. | |
| 30 | + | • Ray for multi-agent execution, Milvus for vector DB, MLflow/Dataiku for MLOps, GitHub Actions/ArgoCD for DevOps. | |
| 31 | + | • TCS accelerators: Datom.ai (ingestion) and AI-Blueprint (governance). | |
| 32 | + | ||
| 33 | + | Key Differentiators | |
| 34 | + | ✔ Multi-agent reasoning at network scale with no single point of failure. | |
| 35 | + | ✔ Self-documenting codebase—every optimisation rule ships with tests & docs. | |
| 36 | + | ✔ Policy-grounded GenAI prevents the chatbot-liability issue from re-occurring. | |
| 37 | + | ✔ Template can be re-used across other carriers, MROs, and rail operators—creating reusable IP for TCS. | |
| 38 | + | ||
| 39 | + | Compliance | |
| 40 | + | All data remains within approved environments; outputs are fully traceable to source policy chunks, satisfying Canada’s pending AI & Data Act. | |
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