EngineeringOwn ModelsHigh complexity
Fine-Tuned Domain Models
Decide when a small model tuned on your own data beats a large general model, and when retrieval alone is enough.
How we approach it
Start with retrieval and a general model, and measure. Fine-tune a smaller open-weight model where the task is narrow and repeated: a fixed output format, a domain vocabulary, tight latency or a cost per call that matters. Keep personal data out of training sets or document its lawful basis, and evaluate against the general model on the same test set.
Business value
Lower cost per call and better accuracy where it counts
Technology stack
- Open-weight models
- LoRA fine-tuning
- evaluation harness
- vLLM
- data pipeline
Related service
Proprietary AI DevelopmentFurther reading
Related use cases
- Cross-Industry · EngineeringModel Retirement & Provider Exit PlanModel providers retire versions at short notice and change prices and terms, and applications tuned to one model break when it goes.
- Cross-Industry · EngineeringLLM Application EvaluationAssistants and agents that looked good in a demo degrade quietly in production when prompts, data or the model change.
- Cross-Industry · EngineeringExpert-Validated Knowledge BaseKnowledge bases built by crawling go stale and fill with pages that are not what they seem, and the assistant on top repeats the errors.