Revision of 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

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• Flight disruptions cost airlines ~8 % of revenue and erode customer trust.

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• Existing optimisation engines react after the fact and require manual rule updates.

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• Recent chatbot-liability incidents show AI outputs must be traceable to policy.

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Solution – AeroSynth™

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1. Data Fabric

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– Ingest live ADS-B, weather, maintenance, crew-roster, and CRM streams into a time-aware vector knowledge graph.

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2. Digital-Twin Core

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– Every flight, crew, gate, and passenger becomes an LLM agent inside a 72-hour forward-looking graph.

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– Agents negotiate swaps in real time; a reinforcement-learning orchestrator balances cost, CO₂, and customer experience.

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3. Passenger Co-Pilot

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– Generates empathetic re-booking messages and vouchers in 50+ languages, each citing the exact tariff or union clause that justifies it.

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4. SDLC Co-Pilot

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– 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.

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Business Impact (first 12 months)

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• 25 % fewer delay minutes ⇒ ≈ CAD $12 M yearly fuel & crew-overtime savings.

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• +2 pt on-time-performance and +8 NPS for disrupted passengers.

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• Extra 10 % reduction in call-centre load by addressing customers proactively.

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Implementation Roadmap

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P0 (6 wks): Pilot on YYZ–YUL corridor; baseline KPIs.

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P1 (3 mo): Deploy twin + swarm with human-in-loop approvals.

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P2 (9 mo): Scale to full network, activate SDLC Co-Pilot, RL cost-tuning.

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P3 (12 mo): Edge inference on wide-body aircraft; open API for alliance partners.

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Tech Snapshot

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• 8 B-parameter open-source LLM fine-tuned on ops + policy corpus.

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• Ray for multi-agent execution, Milvus for vector DB, MLflow/Dataiku for MLOps, GitHub Actions/ArgoCD for DevOps.

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• TCS accelerators: Datom.ai (ingestion) and AI-Blueprint (governance).

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Key Differentiators

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✔ Multi-agent reasoning at network scale with no single point of failure.

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✔ Self-documenting codebase—every optimisation rule ships with tests & docs.

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✔ Policy-grounded GenAI prevents the chatbot-liability issue from re-occurring.

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✔ Template can be re-used across other carriers, MROs, and rail operators—creating reusable IP for TCS.

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Compliance

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

lolpee69 / Idea

lolpee69 revised this gist 1748347616. Go to revision

		@@ -0,0 +1,40 @@
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.