Fine-tuned models for your domain, tone and data
We only recommend fine-tuning after prompting and retrieval are measured. Public named case studies are limited, engagements start with a feasibility check on your data.
What you get with LLM Fine-Tuning & Custom Models
- Check whether prompting or RAG is enough first
- Training data preparation and cleaning
- Fine-tuning on hosted or open-weight models
- Before-and-after evaluation
- Cost and latency comparison
- Deployment and versioning
Phases, timelines and what you receive
Discover
Use-case scope, KPI baseline, data access plan
Pilot
Working pilot on real data with evaluation results
Production
Hardened, integrated, monitored system
Improve
KPI and cost reports, new use cases shipped
Get a plan for LLM Fine-Tuning & Custom Models
A few fields, no sales script. We reply within 1 business day with next steps.
What we build with
Start small, with a clear decision point
Most AI work starts with a workshop or a 4-6 week pilot.
Frequently asked questions
Straight answers on cost, timelines, ownership and data.
Often not. We test prompting and retrieval first and only fine-tune when it measurably improves accuracy or cost.
It depends on the task, but often a few hundred to a few thousand good examples. We assess your data before recommending it.
Not as a standalone published study. Ask us for a relevant walkthrough under NDA.
Ready to talk about LLM Fine-Tuning & Custom Models?
Book a short call with a senior engineer. No pitch deck, just a clear next step.
- We reply within 1 business day.
- NDA available on request
- You own all code from day one