Idea - Opengist

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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.

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.
41