Fine-Tuning Suitability Assessment
An honest read on whether fine-tuning is actually the right tool for your accuracy problem, versus better prompting, retrieval, or a different base model, before you commit budget to training.
Service
Model fine-tuning
When a general-purpose model's accuracy or consistency plateaus on your specific documents and terminology, we fine-tune on your proprietary data, and prove the improvement against a measured baseline before it replaces anything in production.
What it is
Fine-tuning is usually the wrong first move, and part of this service is being honest about when it's the right one. Prompt engineering and retrieval solve most accuracy problems more cheaply and with less operational overhead. Fine-tuning earns its cost when the task requires a consistent output format at volume, a highly specialized vocabulary a general model handles unreliably, or task-specific behavior, such as a particular tone or reasoning pattern for internal risk memos, that prompting alone can't reliably produce.
Where it is the right move, we fine-tune on your proprietary financial data, such as internal memos, historical decisions, your specific terminology and document formats, using parameter-efficient methods that don't require the compute budget of training a model from scratch. Data preparation is most of the work: cleaning, labeling and structuring training examples that actually represent the target task, and holding out a test set the model never sees during training so the evaluation is honest.
Every fine-tuned model is benchmarked against the base model and, where relevant, against the previous production system, on the same held-out eval set, before it is considered for deployment. If the improvement isn't real and measurable, we say so rather than shipping a model that looks different but isn't actually better. Models are versioned, and where the training data includes anything sensitive, training itself runs inside your private infrastructure.
What we build
Capabilities inside Model Fine-Tuning
02
Training Data Preparation
Cleaning, labeling and structuring proprietary examples, such as internal memos, historical decisions and domain terminology, into a dataset that represents the target task, which is most of the actual work.
03
Parameter-Efficient Fine-Tuning
Training using methods like LoRA that adapt a model to your task without the compute cost of full retraining, keeping the engagement proportionate to the problem.
04
Held-Out Evaluation
A test set the model never sees during training, used to measure real improvement rather than the model simply memorizing its training examples.
05
Baseline Benchmarking
Every fine-tuned model measured against the base model and any existing production system on the same eval set, so the improvement is demonstrated, not assumed.
06
Private & Secure Training
Where training data includes sensitive or non-public information, training runs inside your VPC or on-prem infrastructure, keeping the same data boundary as production.
How we work
Delivery process
01Suitability assessment
Confirm fine-tuning is the right approach for the specific accuracy or consistency problem, rather than assuming it before checking cheaper alternatives.
02Data preparation
Clean, label and structure training examples from your proprietary data, holding out a test set the model never trains on.
03Fine-tuning runs
Train candidate models using parameter-efficient methods, iterating on data composition and training parameters.
04Evaluation & benchmarking
Measure every candidate against the base model and existing production system on the held-out eval set.
05Deployment & versioning
Deploy the model that clears the bar, with version control and rollback if a future update underperforms its predecessor.
What to expect
Measured improvement
every fine-tuned model benchmarked against base model and prior system before deployment
3‑6 weeks
typical time from data prep through a benchmarked, deployment-ready model
Held‑out eval
test set the model never sees during training, so results aren't inflated by memorization
Frequently asked questions
How do we know if we need fine-tuning or just better prompting?
We check the cheaper option first. If better prompts or improved retrieval solve the accuracy problem, that's what we'll recommend. Fine-tuning has real ongoing cost (retraining as the base model improves, maintaining the training pipeline) that isn't worth taking on unless prompting has genuinely plateaued.
How much data do we need to fine-tune usefully?
It varies by task, but useful fine-tuning is often possible with a few hundred to a few thousand well-constructed examples, particularly with parameter-efficient methods. Volume matters less than whether the examples actually represent the task and edge cases the model will see in production.
Can training happen without our data leaving our infrastructure?
Yes. Where the training data is sensitive, we run training inside your VPC or on-prem environment, using the same data boundary you would require for production inference.
What happens when the underlying base model gets updated or deprecated?
Fine-tuned models are versioned and tied to a specific base model version. When the provider updates or deprecates the base model, we re-run fine-tuning against the new version and re-benchmark before switching, rather than assuming the improvement carries over automatically.
How do you prevent the fine-tuned model from overfitting to our training examples?
The held-out evaluation set is the main safeguard. A model that overfits typically performs well on training data but not on the held-out set, which is exactly what that measurement is designed to catch before deployment.
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Industries we apply this in
Talk to us about Model Fine-Tuning
A 30-minute call to scope what a first version would look like against your own data and systems.
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