TRAIN & FINE-TUNE
AI Training & Fine-Tuning
Models trained on your data, optimized for your specific domain and use case.
- Training data preparation & curation
- Base model selection & evaluation
- Fine-tuning execution & iteration
- Performance benchmarking & validation
Overview
General-purpose AI models are powerful but generic. When your use case demands domain-specific accuracy — legal terminology, medical coding, financial classification — fine-tuning transforms a general model into a specialist. Vectrel handles the full fine-tuning lifecycle: data preparation, training strategy, model evaluation, and deployment. Fine-tuning is not always the answer. Part of this service is honest evaluation: we tell you when prompt engineering is sufficient, and when true fine-tuning is worth the investment.
Deliverables
What's included
Training data preparation and curation
Sourcing, labeling, cleaning, and formatting datasets to match your model's domain and quality requirements.
Base model selection and evaluation
Benchmarking foundation models against your task to identify the best starting point and justify the selection with data.
Fine-tuning execution and iteration
Iterative fine-tuning with tracked hyperparameters and fully reproducible training runs stored for auditability.
Model performance benchmarking
Quantitative evaluation against holdout datasets and domain-specific metrics to validate real gains over the baseline.
A/B testing against baseline models
Side-by-side comparison of fine-tuned vs. baseline models in simulated production traffic to quantify improvement.
Deployment and serving infrastructure
Hosts the model with auto-scaling, load balancing, and latency targets appropriate for production traffic.
Ongoing model monitoring and retraining strategy
A playbook for detecting model drift, collecting new training signal, and scheduling retraining cycles proactively.
Use Cases
How clients use this
Real Estate Data Extraction
A real estate tech company needed accurate property detail extraction from unstructured listings. We fine-tuned a language model on 50,000 annotated listings, achieving 94% accuracy vs 71% baseline.
Medical Coding Assistant
A healthcare company needed AI that understood specialized medical terminology for automated coding. We fine-tuned a model on their proprietary dataset to achieve domain-specific precision.
Who It's For
Businesses with domain-specific AI needs where general models fall short.
Technologies
Tech stack
Related Services
Often combined with
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