Train stage
Custom Model Development
Best for
Deep domain corpora, regional languages, or edge and air-gapped deployments.
How it runs
Four steps, each with something you keep
- Step 1
Feasibility study
Estimate the tokens, compute, and expected lift before any GPU time is booked.
Go/no-go estimateA GPU-hour range, the expected quality gain, and a recommended model size.
- Step 2
Engineer the corpus
Collect, clean, and balance domain and general text so the model gains fluency without forgetting what it knew.
Corpus pipelineReproducible ingestion, filtering, and de-duplication at pretraining scale.
- Step 3
Pretrain and align
Continued pretraining on the corpus, then instruction tuning and preference alignment.
Domain checkpointA model that has read your corpus and follows instructions.
- Step 4
Compress and document
Distil to the target size, quantise, and benchmark on your hardware.
Deployable model with a model cardFits your latency and memory limits. Data sources, compute, and known limitations are documented for audit.
FAQ
Custom Model Development: common questions
How is this different from fine-tuning?
Fine-tuning teaches an existing model a new task. Custom development changes the model itself: what it knows, which languages it's fluent in, or how small it can be.
What compute budget should we plan for?
Anywhere from a few hundred GPU-hours for a small distilled model to tens of thousands for a large model trained on billions of tokens. You'll have a firm number before committing.
Often paired with
Related services
LLM Fine-Tuning
Adapt an open-weight or hosted model to your tasks, tone, and formats.Deployment & LLMOps
Serve your model with autoscaling, tracing, and retraining loops.
Scope this for your data
Share a sample of your data and a target metric. We'll come back with a plan and a timeline.
We reply within one business day (IST).