What we deliver
Use-case evaluation
A data-driven decision on whether tuning, RAG or prompting wins for your task.
Dataset curation
Collecting, cleaning and labelling representative examples from your domain.
Model customization
Fine-tuning on AWS (Amazon Bedrock) with reproducible pipelines.
Evaluation & rollout
Regression suites, A/B checks and cost monitoring before and after go-live.
Our discipline
- Start from prompting and RAG; fine-tune only when it pays off
- Evaluate on real business cases, not synthetic benchmarks
- Keep data and models inside your AWS tenancy
- Version datasets and models for reproducibility
- Monitor quality and cost continuously after rollout
Related
Frequently asked questions
When is fine-tuning the right choice?
When prompting and RAG plateau on your specific task — tone, format, classification or accuracy on a narrow domain — and the improvement justifies the data and training effort.
How do you decide between prompting, RAG and fine-tuning?
We evaluate all three against your real use cases and metrics, then choose the combination with the best quality, cost and maintainability.
What data do you need?
A set of representative inputs and expected outputs from your domain. We help you curate, clean and label it, and keep it inside your AWS tenancy.
How do you measure improvement?
With evaluation suites that compare the tuned model against baselines on correctness, format, tone and cost — before and after every training run.
Do you fine-tune on AWS?
Yes, using Amazon Bedrock model customization and AWS infrastructure, so data and models stay under your control.
Find out if fine-tuning pays off for you
Send us your task and examples. We'll tell you honestly whether prompting, RAG or fine-tuning is the right investment.
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