AI Model Training
AI Trained on Your World.
What We Do
Generic AI models are trained on the internet. Your business is not the internet. When the language model does not understand your industry terminology, your document formats, or your classification taxonomy, generic outputs are the consequence. Custom model training fine-tunes a base model on your proprietary data so the AI understands your world specifically.
It knows the difference between the contract terms your legal team uses, the product categories in your catalog, and the customer intents your support team encounters. We train language models, classification models, and prediction models on your data and deliver production-ready models you own outright. No vendor lock-in. No subscription required to run a model trained on your own information.
How We Work
Training begins with data preparation: collecting labeled examples, cleaning inconsistencies, balancing class distributions, and structuring the dataset for the training task. We select an appropriate base model from the open-source or commercial ecosystem and run fine-tuning experiments with different hyperparameter configurations. Each experiment is evaluated against a held-out validation set using task-specific metrics. The best-performing configuration is then trained on the full dataset and evaluated against your defined acceptance benchmarks.
Before deployment we document the training process, evaluation results, known edge cases, and operational limits. You receive the model weights, the training dataset, and complete documentation. The model can be served in your infrastructure or deployed through a cloud provider of your choice.
Why Running Start Digital
Pricing
From $10,000
Typical turnaround: 8-16 weeks
Includes
Frequently Asked Questions
When generic models produce inconsistent results on your specific tasks. Industry-specific language, proprietary processes, and unique classification needs all benefit from fine-tuning.
It depends on the task. Classification models can work with a few hundred labeled examples. Complex generation tasks may need thousands. We assess your data and recommend the minimum viable dataset.
Yes. Models trained on your data belong to you. We deliver the model weights, training documentation, and deployment instructions. No lock-in.
We implement evaluation benchmarks, confidence thresholds, and guardrails. The model flags low-confidence outputs for human review rather than guessing.
Data preparation takes 1 to 3 weeks. Training experiments and evaluation take 2 to 4 weeks. Total timeline from data handoff to production-ready model is typically 4 to 8 weeks.
Fine-tuning bakes knowledge into the model weights through training. RAG retrieves relevant documents at inference time and provides them as context. Fine-tuning is better for style, tone, and classification. RAG is better for factual knowledge that changes frequently.
You do. We deliver model weights, training scripts, evaluation reports, and documentation. No ongoing licensing fees. You can serve it, modify it, or transfer it as you see fit.
Ready to get started?
Start with a $5,000 deposit. Balance due on delivery.