Aviation runs on precision. The same standard should apply when choosing an AI vendor.
The market is crowded with platforms claiming domain expertise. Most are general-purpose tools dressed in aviation language.
A few are genuinely built for the industry. Knowing which is which before you sign saves months of painful backtracking.
This framework gives aviation teams a structured set of questions to ask any vendor. For each question, you will see what a strong answer looks like and what a weak one signals.
Why vendor selection matters more in aviation
AI applications in aviation carry real consequences. A poorly configured model in maintenance planning, crew scheduling, or flight operations does not just slow a process down. It creates risk.
The AI solutions for aviation space has matured enough that buyers can and should demand specificity. Vendors who cannot answer these questions clearly are not ready for your environment.
The 10 questions
1. How deep is your domain model knowledge?
General-purpose AI can describe an aircraft maintenance log. A domain-trained model understands how that log relates to airworthiness directives, minimum equipment lists, and regulatory reporting cycles.
Ask the vendor to demonstrate the model on actual aviation content. Ask whether it distinguishes between Part 135 and Part 121 operational contexts without prompting.
| Answer type | What it looks like |
|---|---|
| Good | Accurate aviation terminology handled natively; vendor cites training corpora from MRO, OEM, or operator data |
| Red flag | Generic NLP wrapped in aviation-branded slides; model cannot define ETOPS or distinguish regulatory contexts |
2. Where does your training data come from?
Data provenance matters legally and operationally. A model trained on scraped public data may contain proprietary repair manual content, which creates IP exposure for your organization.
Ask for a data lineage summary. Ask whether any training data came from licensed OEM materials or proprietary operator documentation.
| Answer type | What it looks like |
|---|---|
| Good | Clear lineage with licensed sources, consent records, and IP agreements documented |
| Red flag | ”We use publicly available data” with no further detail on source or licensing |
3. Can your system support certification requirements?
Aviation AI systems that touch safety-critical processes face scrutiny from the FAA, EASA, and ICAO. Even systems used in back-office or commercial functions may require auditability under emerging frameworks.
Ask the vendor how their system supports explainability, logging, and audit trails. Ask whether they have engaged with regulators on AI governance standards.
| Answer type | What it looks like |
|---|---|
| Good | Documented logging architecture, explainability outputs, and active engagement with DO-178C or EASA AI Roadmap frameworks |
| Red flag | ”We are working on compliance” or “that depends on how you use it” |
4. What deployment options do you offer?
Not every aviation operator can or should run AI in a public cloud environment. Sensitive flight data, proprietary scheduling algorithms, and personnel records may require on-premise or air-gapped deployments.
Ask whether the platform supports on-premise installation, private cloud, and fully disconnected environments. Ask who controls the compute infrastructure in each scenario.
| Answer type | What it looks like |
|---|---|
| Good | Documented support for on-premise and air-gapped deployment with named reference customers in each model |
| Red flag | Cloud-only architecture with no roadmap for isolated or sovereign deployment environments |
5. How does your system integrate with our existing tools?
Aviation operations run on a mix of ERP systems, maintenance tracking platforms, and crew management software. An AI vendor that requires wholesale replacement of existing systems creates unnecessary deployment risk.
Ask which platforms they have pre-built connectors for. Ask what the typical integration timeline looks like for a mid-complexity aviation environment.
| Answer type | What it looks like |
|---|---|
| Good | Named integrations with major aviation platforms; documented API layer with versioned and backward-compatible endpoints |
| Red flag | ”We integrate with everything” without specifics or without a customer reference to confirm |
6. How do you handle data security and sovereignty?
Aviation operators in the EU, Middle East, and Asia-Pacific face strict data residency requirements. Even US operators may have contractual obligations with government customers that restrict where data can be processed.
Ask where your data is stored, who can access it, and how the vendor handles cross-border data flows. Ask for their SOC 2 Type II report or equivalent third-party certification.
| Answer type | What it looks like |
|---|---|
| Good | Documented data residency options, SOC 2 Type II certification, and a clear list of subprocessors with access scope |
| Red flag | Unable to confirm where your data lives or who within the vendor organization can access it |
7. What are your support SLAs?
An AI system embedded in flight operations or maintenance workflows is not a convenience tool. Downtime has a direct operational cost.
Vendors who cannot commit to defined response and resolution times are not equipped for aviation environments. Verbal assurances are not SLAs.
| Answer type | What it looks like |
|---|---|
| Good | Tiered SLAs with sub-four-hour response for critical incidents and a named technical account manager for escalation |
| Red flag | Support via ticketing portal only; no guaranteed SLA; 24-hour response windows for production-critical issues |
8. Is your pricing transparent and tied to outcomes?
AI vendor pricing models range from per-seat licenses to consumption-based billing to outcome-linked fees. Hidden costs often appear at the integration, training, or scaling stage.
Evaluating the ROI and business case for any AI investment requires a complete picture of total cost of ownership, not just the headline license fee.
Ask for a complete cost breakdown including onboarding, integration, training data preparation, and scale pricing. Ask whether pricing changes if your usage doubles.
| Answer type | What it looks like |
|---|---|
| Good | Itemized pricing across all deployment phases with a cap or defined formula for scale costs |
| Red flag | Vague “let’s discuss” pricing; quotes that exclude implementation, training, and ongoing support costs |
9. Can you provide aviation-specific reference customers?
A vendor with genuine aviation experience should be able to connect you with customers operating in your segment. An airline buyer should speak with another airline. An MRO should speak with another MRO.
The best aviation tools are validated by operators who have deployed them through real operational conditions. References in adjacent industries are not the same as production deployments in aviation.
Ask for at least two references in your operating segment. Ask specifically about the deployment timeline and how problems were resolved.
| Answer type | What it looks like |
|---|---|
| Good | Named references willing to speak candidly; at least one operates in your specific aviation segment |
| Red flag | References limited to adjacent industries; testimonials provided in lieu of direct customer contact |
10. What are the exit and data portability terms?
Vendor lock-in is a structural risk. If the relationship ends, you need to recover your data in a usable format, not a proprietary export that requires the vendor’s own tools to read.
Ask what happens to your fine-tuned models, your training data, and your output logs if you cancel the contract. Ask about notice periods and whether transition support is included.
Seeking AI consulting guidance before signing can prevent contractual structures that limit your future operational flexibility.
| Answer type | What it looks like |
|---|---|
| Good | Data export in standard formats (JSON, CSV, ONNX for models); transition support period defined in the base contract |
| Red flag | Data stored in proprietary format; exit fees not disclosed upfront; fine-tuned models treated as vendor IP |
A quick scoring approach
Before meeting with vendors, turn these 10 questions into a scorecard. Rate each answer on a simple scale:
- 3 - Specific, documented, and verifiable with evidence
- 2 - Plausible but unverified; request supporting documentation before advancing
- 1 - Vague, deflected, or absent
Any vendor scoring below 20 out of 30 warrants serious caution before you advance to a contract stage. Any vendor scoring below 15 should not progress further.
Use this scoring in committee. Aviation vendor decisions made by one person without structured review create single points of failure in an already high-stakes process.
Making a confident vendor decision for your aviation operations
The vendor selection process sets the ceiling on what your AI investment can achieve. A wrong choice delays operations, creates compliance exposure, and is expensive to unwind.
Vendors who cannot answer detailed questions about how their model handles your specific data and use case are vendors who have not deployed aviation AI in production.
Path one: run all ten evaluation questions through every vendor on your shortlist. Use the questions in this guide as a standardised scorecard. Score each vendor on a five-point scale for each question and compare the totals. Vendors who cannot answer clearly on aviation-specific data handling and integration should not advance past the first review.
Path two: bring in a partner. Phos AI Labs designs AI implementations for aviation organisations; aviation AI vendor evaluation and selection, compliance integration, and the private AI environment your team will actually use. We have run 400+ AI engagements. Clients include Zapier, Coca-Cola, Medtronic, Dataiku, and American Express. Thirty minutes, no deck. Start here.
Related articles