05 · AI Implementation
Fine-Tuning & Inference
Specialised models that beat general-purpose ones on accuracy and unit cost.
Overview
What you get
Dataset curation, fine-tuning across multiple base models, deployed inference with autoscaling, and a drift-aware eval suite.
The problem
Why teams call us
- General-purpose models are expensive at your volume — and not better.
- Domain-specific terms, formats, or refusals aren't handled well.
- Drift over time is invisible until accuracy quietly halves.
Approach
How we work
- Curate a focused dataset with a labeling rubric we co-write.
- Fine-tune at least two base models and benchmark them honestly.
- Deploy with autoscaling and a CI eval suite that catches drift.
Process
Week by week.
- 01 · Week 1–2
Dataset
Sampling, labeling rubric, train/eval splits.
- 02 · Week 3–5
Tune & benchmark
Fine-tune across 2 base models with eval harness.
- 03 · Week 6–7
Deploy
Inference service with autoscaling in your cloud.
- 04 · Week 8
Monitor
Drift detection, eval CI, handoff.
- A bounded task where general models cost or underperform
- Access to representative labeled data (or a path to it)
- Appetite for an eval harness to measure regressions
- Open-ended general chat use cases
- Datasets too small or too noisy to support tuning
- Teams unwilling to operate a model post-deploy
Deliverables
Everything we ship
- 01Dataset curation and labeling rubric
- 02Fine-tune across two base models, benchmarked
- 03Inference deployment with autoscaling
- 04Eval suite + drift monitoring
- 05Cost-per-1k-tokens + latency dashboards
Outcomes
What you walk away with.
A specialised model that beats your current general-purpose baseline on accuracy and unit cost — with a path to keep it that way.
Tooling
Stack we ship against
Model- and infra-agnostic. We adapt to your stack, not the other way around.
FAQ
Real questions, technically answered.
- Open-source or closed-source base model?
- We benchmark both. The decision falls out of the numbers, not vibes.
- Where does inference run?
- Your cloud (AWS, GCP, Azure) or a managed provider — your call, with cost modeled both ways.
- How do you handle re-training?
- Eval CI flags drift; re-training cadence is part of the handoff playbook.
Related engagements
Often paired with.
04 · AI Implementation
RAG & Embedding
Production-grade retrieval — measured against your golden set before it ships.
07 · AI Infrastructure
Cloud Migration
Move AI workloads off third-party APIs and into your own cloud — without downtime.
09 · AI Infrastructure
Production Telemetry
When your AI gets worse, your team knows in minutes — not quarters.
Next step
Ready to scope Fine-Tuning & Inference?
Book a discovery call. We'll confirm fit, sequence the engagement, and have a Statement of Work in your inbox within a week.
Refundable if we're not a fitWritten diagnostic in 48 hoursSession run by a founder, not a sales rep