How can aviation companies use RAG for internal knowledge?

Key facts

API surface	OpenAI-compatible /v1/chat/completions; keep your existing SDK
Grounding	Embeddings and RAG are live for flight ops, maintenance and training search
Models	30+ models behind one API, from free tiers to frontier
Deployment	Plugsky cloud, your VPC, on-prem or air-gapped
Access control	Scoped API keys, rotation and usage analytics; enterprise SSO and RBAC options
Auditability	Request and response logging with usage analytics
Pricing model	Flat monthly self-serve plans; no per-token billing on self-serve
Free tier	Free plan with plugsky-micro and plugsky-lite; 14-day full-access trial

TL;DR

• Ground aviation answers in approved internal content with RAG instead of model memory.
• Keep safety-critical or airworthiness content inside VPC, on-prem or air-gapped deployments where policy requires it.
• Scope retrieval per team, client or site so permissions and confidentiality hold at query time.
• Keep a named human owner for every operational or airworthiness decision.
• Index only approved revisions and cite them on every answer.

How it works, step by step

1. Inventory the content to make searchable: flight operations manuals, maintenance documentation, training material and safety procedures.
2. Define a data boundary for the pilot that excludes restricted material until controls are proven.
3. Choose a deployment target: cloud for public content, VPC, on-prem or air-gapped for restricted data.
4. Build an evaluation set with training captains and maintenance planners so answer quality is judged by domain experts.
5. Ingest, chunk and embed approved documents, and require citations on every answer.
6. Add refusal behavior for questions outside the indexed, approved content.
7. Review logged interactions on a schedule and expand only after accuracy and access checks pass.

Original data

Try it yourself

Open the RAG sandbox →

Where aviation teams start

Start with internal questions that already have a written answer. High-value first workloads include:

• Flight operations manuals: answer procedure questions from approved manuals and revisions.
• Maintenance documentation: retrieve task cards and service information for engineer sign-off.
• Safety and SMS records: surface safety reports, risk assessments and mitigation notes.
• Training and checklists: answer from syllabi, checklists and competency material.

Each use case augments staff with cited answers; none replaces qualified post-holder judgment.

A private RAG architecture for aviation knowledge

The stack is consistent across industries: ingest approved flight operations manuals, maintenance documentation, training material and safety procedures, chunk and embed with a multilingual embedding model, store vectors inside your environment, and call chat completions that answer only from retrieved context. Plugsky embeddings and chat completions are OpenAI-compatible, so teams already using OpenAI SDKs change the base URL and keep their code.

Revision metadata is mandatory: filter retrieval to the approved revision for the aircraft type and operator. Store vectors in the same environment as the manuals and log which revision was cited for every answer.

Access control, confidentiality and audit

Safety-critical content must stay revision-controlled: index only approved versions, expire superseded documents, and always show the source revision with the answer. Access filters should separate operational, maintenance and confidential program material, with logs retained for safety and audit review.

The technical controls are consistent: enforce permission-aware retrieval in your own service layer, scope API keys per application, team or tenant, rotate keys, and retain request and response logs on a defined schedule. Regulatory obligations vary by jurisdiction and sector, so map them with counsel rather than assuming one framework covers every deployment; Plugsky supplies the deployment and logging primitives you document.

See air-gapped AI for related deployment and control detail.

Rollout and human oversight

Pilot on training and general operations material before touching safety-critical or airworthiness content. Build a labelled question set with training captains and maintenance planners, then measure retrieval hit rate, citation correctness and answer accuracy before and after every index or model change. Require citations on every answer, refuse out-of-scope questions, and name a human owner for every operational or airworthiness decision.

Review logged interactions weekly at first, correct the index rather than the prompt when retrieval misses, and expand the corpus only when accuracy and access checks pass.

Honest comparison

Capability	Plugsky	Public AI assistants	Building in-house
Data boundary	Cloud, VPC, on-prem or air-gapped	Vendor cloud only	You control fully
Grounding	Embeddings and RAG are live for flight ops, maintenance and training search	Uncontrolled retrieval	You assemble and operate
Access control	Scoped keys, usage analytics, enterprise SSO and RBAC options	Account-level only	Custom identity work
Auditability	Request and response logging	Limited	You build logging
Pricing	Flat monthly self-serve plans; see live pricing	Per-seat or per-token	GPU plus operations cost
Time to pilot	Days	Hours, without residency control	Quarters

Frequently asked questions

Can aviation organizations keep data private with Plugsky?

Yes. Choose the deployment boundary that matches the data: Plugsky cloud for public content, or your VPC, on-prem and air-gapped options for restricted material. Access is controlled with scoped API keys and usage analytics.

Do we need to fine-tune on our internal documents?

Not for a first release. Fine-tuning is coming soon and is better for style than facts. RAG keeps answers current, permission-aware and traceable to a source, which matters more for internal knowledge.

Which model should we use?

Start free with plugsky-micro and plugsky-lite to validate retrieval, then evaluate mid-tier and frontier models from the 30+ model catalogue on your own question set.

How do we stop wrong or unsupported answers?

Restrict the assistant to approved indexed content, require citations, refuse out-of-scope questions and keep a human decision-maker for every regulated or client-facing outcome.

How is pricing structured?

Self-serve plans are flat monthly with unlimited fair-use usage, and there are no per-token charges on self-serve plans. See the live pricing page for current plans and the free tier.

Which documents should be indexed first?

Start with training material and general operations manuals. Add maintenance and safety-critical documents only after revision control and citation accuracy are proven.

How do we keep answers on the approved revision?

Index only approved revisions, expire superseded documents at ingestion, and display the source revision with every answer so staff can verify it.