Defence & Aerospace

Custom Model Fine-tuning for Defence & Aerospace

Custom Model Fine-tuning 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

Most teams who ask for fine-tuning need better prompting and retrieval instead. We check that first, and say so when it is true. It saves you a quarter and a budget line.

In defence & aerospace, systems must run fully air-gapped, on open weights, with no external dependency whatsoever. That single fact reshapes how custom model fine-tuning 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 logistics and inventory optimisation, usually integrated against simulation platforms. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

Deployed across regulated and unregulated sectors, with audit trails where the regulator expects them. You own the code, the models where they are open-weight, and the documentation to run it without us.

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

Custom Model Fine-tuning workloads in defence & aerospace

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

What is included

  • Honest assessment of whether fine-tuning is warranted
  • Training data curation and quality review
  • LoRA or full fine-tune as the workload justifies
  • Evaluation against the prompted baseline
  • Inference deployment and cost comparison
  • Retraining pipeline as your data grows

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.

Should we fine-tune?

Usually not first. Prompting and retrieval solve most problems more cheaply. Fine-tuning wins for consistent format, narrow domain style, and high-volume tasks where a smaller model can replace a larger one.

How much data do we need?

For LoRA on a narrow task, often a few thousand high-quality examples. Quality matters far more than volume. We review the dataset before training anything.

Can we own the model?

With open-weight base models, yes. You hold the weights and can run them on your own infrastructure indefinitely.

Custom Model Fine-tuning 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