Service · Assessment
P4Fine-Tuning Feasibility & Data Readiness.
Half the time, fine-tuning isn't the right answer. The other half, it is — and worth the investment. In two to four weeks we tell you which category you're in, with evidence, and hand you the costed build proposal if the answer is yes.
Outcomes
What changes when the engagement lands.
An honest answer
Fine-tune, RAG, or prompt engineering — whichever is right for your case, defended with evidence.
Data readiness read
Whether your training data is where it needs to be, and what to fix if not.
Base model shortlist
Three to five candidate base models, ranked for your use case with rationale.
Costed build proposal
If fine-tuning is right, you leave with a proposal ready to sign — from us or from anyone else.
Deliverables
What's in the engagement.
A two- to four-week structured feasibility engagement that decides — with evidence — whether fine-tuning is the right answer for your use case, or whether RAG or better prompting delivers the outcome cheaper. Includes data readiness audit, base model shortlist, and a costed build proposal if fine-tuning is the answer.
Recommendation memo
The decision, with the evidence behind it. Fine-tune, RAG, prompting, or a combination.
Data audit
Your training data assessed against the recommendation. Gaps, quality issues, and fix cost.
Base model shortlist
Ranked model options with rationale, licensing implications, and expected cost.
Costed build proposal
If the recommendation is fine-tune, the proposal you'd sign to run it — scope, price, timeline.
How we deliver
Fixed scope. Named phases. Duration on the cover.
The engagement is priced against the outcome, not open-ended hours. Every phase has a duration, a named deliverable, and a check-out.
Total duration2 to 4 weeks
Sales cycle3 to 6 weeks
- 01Days 1 to 3
Use case scoping
Interview stakeholders. Confirm the outcome the AI has to produce. Success criteria agreed.
- 02Days 4 to 10
Data audit
Training data inventory. Quality assessment. Gap analysis.
- 03Days 11 to 15
Approach comparison
Fine-tune vs RAG vs prompting evaluated for your use case. Base model options assessed.
- 04Days 16 to 20
Read-out
Recommendation memo delivered. Executive read-out. Costed build proposal if applicable.
Built for
Buyers this engagement fits.
Typical buyer
CTO, Chief Data Officer, Head of AI
CTOs weighing a fine-tune investment
The team wants to fine-tune. The board wants to know why not RAG. The engagement produces the answer.
Businesses evaluating multiple AI vendors
Vendors all claim their approach is best. Get the outside read on which is best for you.
Chief Data Officers checking training data readiness
The build depends on the data. Know if the data's ready before the build starts.
Related services
Where this leads next.
Domain Adaptation Pilot
Dataset prep. LoRA or QLoRA fine-tune. Evaluation harness. Hosted pilot endpoint.
Production Fine-Tune & Deployment
Reproducible pipeline. Multiple adapters. Serving stack. Guardrails. Provenance. Client IP.
Data Readiness for AI
Data quality, access, structure, residency assessed. Fix plan. The blocker before any build.
Talk to us.
45 minutes on your operation and the engagement you have in mind. No pitch, no deck.
Lead intake
Request a briefing
A 45-minute call. No pitch, no deck. We ask the questions we'd ask a Discovery client and tell you honestly whether this is the right next move.