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AI deployment for enterprise marketing teams.
© 2026 FutureProof LLC
General models plateau on specialized work. Post-training on a smaller, targeted dataset makes a model measurably better at your vertical task — your taxonomy, your tone, your edge cases. We handle data prep, training, evaluation, and deployment.
You've written the ten-page prompt. You've stuffed the context window with examples. The model still misclassifies your edge cases, drifts off your taxonomy, and writes in a tone that's almost — but not quite — yours. Past a certain point, no amount of prompting closes the gap, and paying frontier-model prices for every call doesn't either.
Fine-tuning closes it differently: train the behavior in instead of describing it every time. A smaller model post-trained on a few thousand of your best examples gets your task right more often, runs faster, and costs a fraction per call. The catch is that the work is mostly data and evaluation — and that's the part we do well.
From use-case selection through deployment and retraining. You get the model, the dataset, the evaluation harness, and the numbers that justify all three.
We pick the use case and base model with you, curate the dataset from your real examples, run the training, and measure the result against the untuned baseline on your actual tasks. If tuning doesn't beat the baseline, you'll know before it ships — not after.
Scope your use case →The evaluation harness comes first. We benchmark the base model on your task, tune, and benchmark again — same tasks, same scoring. Every claim about improvement is a number you can re-run yourself, and the harness keeps working long after we leave.
Ask about evaluation →A tuned model that never retrains slowly falls behind your data. We deploy with monitoring, define what triggers a retrain, and set the cadence — so the model keeps up as your taxonomy, products, and edge cases change.
Ask about operations →Fine-tuning pays off when the task is specialized, repeatable, and running at volume.
Fine-tuning works best on clean data with a governed route to production. These services cover both.
A scoping call takes 30 minutes. You leave knowing whether your task is a fine-tuning candidate and what the benchmark would look like.
AI deployment for enterprise marketing teams.