Defence & Aerospace

RAG & Knowledge Retrieval for Defence & Aerospace

RAG & Knowledge Retrieval for defence & aerospace, built around the constraint that defines the sector: systems must run fully air-gapped, on open weights, with no external dependency whatsoever.

Regulations in scope
4
Systems we integrate
4
Typical first release
6 weeks

What changes when it is defence & aerospace

Permission-aware retrieval is not optional in an enterprise. If a user cannot open a document in SharePoint, the assistant must not quote it. We enforce that at the retrieval layer, not in the prompt.

In defence & aerospace, systems must run fully air-gapped, on open weights, with no external dependency whatsoever. That single fact reshapes how rag & knowledge retrieval has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is imagery analysis, usually integrated against logistics systems. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.

Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.

The sector constraints we design around

Defining constraint
systems must run fully air-gapped, on open weights, with no external dependency whatsoever
Regulations in scope
security clearance requirements · indigenous content norms · export control · classified handling procedures
Systems of record
classified networks · logistics systems · simulation platforms · sensor systems
Where we usually start
document intelligence on classified material

RAG & Knowledge Retrieval workloads in defence & aerospace

  • document intelligence on classified material
  • imagery analysis
  • logistics and inventory optimisation
  • maintenance prediction
  • training simulation support

What is included

  • Ingestion pipeline for your real document formats
  • Chunking and embedding strategy tuned to your corpus
  • Hybrid keyword plus vector retrieval with reranking
  • Citations on every answer, traceable to the source page
  • Permission-aware retrieval that respects existing access rules
  • Retrieval quality benchmarked against a labelled question set

Questions from this sector

Can it work fully offline?

Yes, open-weight models on local infrastructure, with no external API calls at any point in the pipeline.

What about indigenous requirements?

Open-weight models deployed on Indian infrastructure with source-available components satisfy most indigenous content criteria; we structure builds accordingly.

RAG or fine-tuning?

RAG for knowledge that changes and must be cited; fine-tuning for style, format and task behaviour. Most production systems use RAG for the facts and light fine-tuning or few-shot prompting for the form.

How accurate will it be?

We build a labelled question set from your domain and report retrieval precision and answer accuracy against it. That number is the deliverable. We do not ship a system whose quality nobody has measured.

Can it respect our existing permissions?

Yes. Retrieval is filtered by the user's actual entitlements, so the assistant can never surface a document the user could not already open.

RAG & Knowledge Retrieval for defence & aerospace, worth a conversation?

Tell us the workload and the regulation it sits under. We will tell you what is realistic.

Or email bd@dtrasglobal.com · call +91 74118 77878