A general-purpose model knows a lot about everything. A fine-tuned model knows everything about your thing. We create the datasets and run the training that makes the difference.
Frontier models are trained on everything. They're remarkably capable generalists. But for domain-specific tasks — classification, style matching, structured output generation, specialist knowledge — a model trained specifically on your data and your requirements will outperform a generalist every time.
Fine-tuning is only as good as the dataset behind it. We spend most of our time on the data — because that's where the results come from.
What exactly should the model do? What's a good output? What's a bad one? What are the failure modes we're training against? Clear task definition before touching any data is what separates effective fine-tuning from expensive experiments.
We design and build the training dataset — either from your existing data (production records, historical examples, domain documents) or through structured synthetic data generation. Pairwise datasets that show the model exactly what good looks like versus what doesn't work.
Which base model gives us the best starting point for your task? We use our Squirmify evaluation framework to test candidates before committing to a training run. Sometimes the right answer is a 7B model, not a 30B one.
Training runs on our local GPU cluster — your data doesn't go to a third-party training service. Rigorous evaluation against held-out test sets throughout. We iterate on the dataset, not just the hyperparameters.
Final model quantised for efficient local inference and deployed via your preferred serving infrastructure. You own the weights. Nothing is held in a vendor's cloud. Ongoing evaluation to catch drift as your domain evolves.
When you need a model to reliably categorise inputs according to your taxonomy — production routing, content classification, risk assessment, triage. General models drift on edge cases. Specialist models don't.
Training AI to write in a consistent voice — your brand tone, your technical terminology, your communication norms. Fine-tuned models for communication tasks produce output that's indistinguishable from the target style.
When you need reliable, consistently formatted output from variable inputs — structured data extraction, report generation, form completion. Fine-tuning makes the format consistent in a way that prompting alone can't guarantee.
Embedding hard constraints into model behaviour — ensuring it never hallucinates specific categories of information, always follows specific escalation logic, or maintains specific safety guardrails. Guardian is the proof of concept here: zero hallucinated crisis resources under any input.
Describe what you're trying to do and what good looks like. We'll tell you whether fine-tuning is the right approach and what dataset creation would involve.
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