Capability
Custom Model Fine-tuning across India
Fine-tuned and distilled models for your domain, cheaper, faster and more consistent than prompting alone.
- Industries
- 12
- Stack options
- 8
- Typical first release
- 6 weeks
What custom model fine-tuning means when we build it
Orqent Labs fine-tunes and distils models for teams with genuine volume, where the economics of inference have started to matter more than the ceiling of capability.
We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.
Built by engineers who ship production systems, not by a practice that subcontracts the build. You own the code, the models where they are open-weight, and the documentation to run it without us.
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
Who this is for
We usually work with ML leaders, AI product teams and CTOs with scale workloads, the people who own the outcome rather than the tooling decision.
Custom Model Fine-tuning by industry
Each sector changes the constraints, regulation, systems of record, and what a wrong answer costs.
- Custom Model Fine-tuning for Healthcare & HospitalsDPDP Act 2023
- Custom Model Fine-tuning for Pharmaceuticals & Life SciencesCDSCO
- Custom Model Fine-tuning for Legal ServicesBar Council rules
- Custom Model Fine-tuning for Financial ServicesRBI guidelines
- Custom Model Fine-tuning for BankingRBI master directions
- Custom Model Fine-tuning for InsuranceIRDAI regulations
- Custom Model Fine-tuning for SaaS & TechnologySOC 2
- Custom Model Fine-tuning for TelecommunicationsTRAI regulations
- Custom Model Fine-tuning for Government & Public SectorDPDP Act 2023
- Custom Model Fine-tuning for Defence & Aerospacesecurity clearance requirements
- Custom Model Fine-tuning for Education & EdTechDPDP Act 2023
- Custom Model Fine-tuning for Media & Entertainmentcopyright law
Custom Model Fine-tuning, stack options
We pick per workload. Each page states the honest trade-off.
- Custom Model Fine-tuning with Llamamodel
- Custom Model Fine-tuning with Mistralmodel
- Custom Model Fine-tuning with PyTorchframework
- Custom Model Fine-tuning with Pythonframework
- Custom Model Fine-tuning with AWS Bedrockplatform
- Custom Model Fine-tuning with Azure OpenAIplatform
- Custom Model Fine-tuning with NVIDIA Jetsoninfra
- Custom Model Fine-tuning with Databricksdata
Questions we get asked
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.
Considering custom model fine-tuning?
Tell us the workflow and the constraint. We will tell you honestly whether it is worth building.
Or email bd@dtrasglobal.com · call +91 74118 77878