Aviation operations under safety and customer-data scrutiny
AI inside the operator's perimeter. Evidence-ready by design.
deeplinq sits on top of your existing aviation stack — ARMS, AMOS, GDS, EFB, FRMS — and runs operations, MRO, customer-service, and crew workflows on context drawn from those systems. Architecture stays inside your perimeter. Decisions stay with your licensed staff. Every interaction is archived for safety, compliance, and DPO review.
This page covers commercial aviation — passenger and cargo operators, MRO organisations, and commercial flight training. Defense, military, and classified flight contexts are out of scope of this page.
Why aviation does not fit a generic AI platform
A layered envelope no off-the-shelf AI was built to fit.
Operators sit inside a layered envelope. Aviation safety frameworks govern dispatch, maintenance, and crew. Regulator scrutiny on AI use is rising in parallel. The passenger-data envelope sits alongside, framed by national data-protection law. No off-the-shelf AI platform was built to fit this stack.
deeplinq deploys connectors into the systems that already run the operation — ARMS, AMOS, Sabre or Amadeus, Lufthansa Systems Lido, EFB content management, FRMS. The knowledge graph harmonises operations, MRO, customer, and crew state across those sources. Plain-language queries return sourced answers with a citation trail. Every prompt, retrieval, and agent action is archived.
Your national data-protection regime — GDPR, PDPL, Loi 09-08, nLPD, or equivalent regional frameworks — frames passenger-data handling. Aviation safety frameworks (ICAO, EASA, FAA, national CAA) sit alongside. Defense, military, and classified contexts are out of scope of this page.
Workflow 1 — Operations control
Sourced context for the dispatcher, not a replacement for the decision.
Flight dispatch, weather, NOTAMs, and IRROPS context sit across ARMS, dispatch consoles, weather feeds, CRS messaging, and NOTAM feeds. During disruption, the right context is rarely on one screen.
deeplinq deploys connectors into ARMS, dispatch consoles, weather feeds, CRS messaging, and NOTAM feeds. The knowledge graph harmonises operational state across those sources. Agents surface sourced context during incidents — preparing dispatch decisions with a citation trail back to the originating record. Plain-language queries return answers framed inside the dispatcher's existing console.
Aligned with EASA Part-OPS, ICAO Annexes 1, 6, and 8, and EU 376/2014 occurrence reporting. deeplinq does not replace dispatch decision authority. Operational decisions remain with the Pilot in Command and the dispatcher under their licence.
Workflow 2 — MRO knowledge
Maintenance documentation, surfaced with citations back to source.
Maintenance manuals, service bulletins, airworthiness directives, and parts traceability sit across AMOS, paper records, manufacturer portals, and vendor systems. Pre-task lookup costs hours.
deeplinq deploys connectors into AMOS, manufacturer technical publications, AD and SB feeds, parts inventory, and vendor portals. The knowledge graph links each tail number to AD and SB applicability and maintenance history. Agents surface technical answers with citations back to manual page, AD number, or SB reference. Pre-task and pre-flight technical queries return sourced context for the certifying staff.
Aligned with EASA Part 145, Part M, Part 21, FAA Part 145, and ISO 9001. deeplinq is platform, not Type Certificate holder. deeplinq does not certify airworthiness. Maintenance certification stays with the Part 145 organisation under licensure.
Workflow 3 — Customer service
Multilingual passenger context, drafted from a unified view.
Multilingual passenger care, IRROPS handling, and complaint workflows depend on context fragmented across PNR, CRM, loyalty, recorded-call archives, and complaint history.
deeplinq deploys connectors into PNR, CRM, the loyalty platform, recorded-call archives, and complaint history. The knowledge graph harmonises passenger context across those sources. Agents surface multilingual sourced context for L1 and L2 customer-care staff during IRROPS or routine calls. Subject-access-request response drafts are produced from the unified view, leaving review and release with the agent.
Frameworks the workflow respects: your national data-protection regime — GDPR Article 15 in the EU, PDPL Article 13 in the UAE, Loi 09-08 Article 7 in Morocco, nLPD Article 25 in Switzerland, and equivalent rights frameworks elsewhere. The agent stays in control — deeplinq does not auto-rebook and does not auto-issue compensation.
Workflow 4 — Crew operations
Roster, qualifications, and EFB queries answered against a single state.
Roster, fatigue management, qualifications currency, and EFB updates sit across the HR-crew system, ops, training records, and FRMS. The crew controller pieces them together by hand.
deeplinq deploys connectors into crew rostering, qualifications, training records, EFB content management, and FRMS. The knowledge graph harmonises crew state across those sources. Agents surface roster questions, qualifications-currency checks, and EFB technical queries — every answer cited back to the source training record, roster entry, or document version.
Aligned with EASA Part-FCL, Part-MED, ICAO Annex 1, and fatigue management frameworks (CASA, FAA AC 120-103A, EASA FTL). deeplinq does not replace crew dispatch decision. Crew availability calls remain with the crew controller under licensure.
Evidence layer
A regulator inquiry is passed on evidence, not on claims.
A regulator inquiry, an internal audit, a Just Culture review — none is passed on claims. Each is passed on evidence. deeplinq treats the evidence layer as a first-class platform concern. Every prompt, retrieval, model call, and agent action is archived with full context, source attribution, and an RBAC trace tied to the user who issued it.
Model-version pinning is the structural backbone. The model that produced an output during one audit cycle reconstructs the reasoning during the next. Reporting templates, retention parameters, and lawful-basis annotations stay versioned alongside the interactions they cover. Exports run in formats your safety, compliance, and DPO functions already submit.
deeplinq does not certify aviation safety compliance — no platform can. Compliance stays a property of the operator, the safety management system, and the certifying staff under licensure. What deeplinq produces is the evidence trail those functions expect to see.
Deployment
Residency shaped by the data, not by a single answer.
Aviation residency is not one question. It is several. Passenger data is shaped by your national data-protection regime. Operational data is framed by safety reporting obligations. Crew data sits under labour and licensing rules. Manufacturer technical documentation is held under contractual handling clauses. The deployment topology has to fit each.
deeplinq supports four deployment modes — on-premise inside the operator's data centre; customer-tenanted private cloud on AWS, Azure, or Google Cloud; a regional sovereign cloud aligned with national hosting requirements; and a deeplinq-managed cloud where the compliance posture allows it.
Model choice is held behind an interface the operator controls. Cloud APIs for non-sensitive operational queries, open-weights for passenger-data and crew workloads. The full model-agnosticism posture is detailed in the model-agnosticism section on /banking-regulated.
Start with the workflow where evidence and operations meet
Start a conversation. Not a sales process.
A working session with our team — on your operational envelope, your safety reporting frameworks, your deployment constraints, and the operations, MRO, customer-service, or crew workflow where the evidence posture matters most for your operator. Pragmatic, technical, short.