Model Fine Tuning
Prompting gets you far. When you need consistency on a narrow task, lower latency, or domain-specific outputs that prompt engineering can't deliver, fine-tuning is the next step. We handle the full loop: dataset, training, evaluation, deployment.
Overview
Fine-tuning is overused early (when prompts would work) and underused later (when prompts are hitting a ceiling). We help you decide when it makes sense, prepare the dataset properly, run the training, and evaluate rigorously before you ship.
Why choose this service
Most fine-tuning projects fail on the data, not the training. We focus on dataset design, labeling, and validation before a GPU runs.
Held-out test sets, domain-specific metrics, and head-to-head comparisons with strong prompting baselines.
Supervised fine-tuning, LoRA adapters, DPO, or classic ML. We pick what fits, not what's trendy.
Containerized models, monitoring, and retraining pipelines so the tuned model stays good as data evolves.
How we work
Prove prompting won't work first. Measure where it falls short and whether fine-tuning can close the gap.
Gather, clean, label, and validate the training data. Usually the longest step.
Run training, evaluate on held-out data, compare against baseline, iterate on data and hyperparameters.
Containerize, deploy, and set up drift monitoring and scheduled retraining.
Applications
Technologies
FAQ
Usually prompting first. We fine-tune when prompting can't hit your accuracy, latency, or consistency bar.
A few hundred well-labeled examples for narrow tasks, thousands for broader ones. Quality matters more than volume.
Explore more
Tell us about your product. We'll tell you how we'd build it, and how fast.