I work with teams that have repeated, measurable AI tasks where generic prompting or retrieval is no longer producing sufficient accuracy, consistency, latency or cost performance.

The engagement starts by determining whether fine-tuning is actually the right solution.

We establish a baseline, build a representative evaluation set and compare alternatives such as:

Prompt/system improvements

Retrieval/RAG

Tool use

Structured deterministic logic

Model fine-tuning or task-specific training

If model adaptation is justified, I build and evaluate the training pipeline.

Use-case and target-metric definition

Dataset review and cleaning

Data preparation and train/validation/test splits

Baseline model evaluation

Supervised fine-tuning

LoRA/QLoRA/PEFT where appropriate

Embedding or reranker tuning

Experiment tracking

Held-out evaluations

Failure-mode analysis

Accuracy/cost/latency comparison

Model card and documentation

Deployment packaging

Monitoring and rollback plan

Finance/risk classification

Domain-specific extraction

Structured decision support

Ranking and retrieval

Customer-specific terminology

Repetitive high-volume language tasks with labelled examples

Very small/noisy datasets, frequently changing requirements, or problems where current facts should simply live in retrieval.