Off-the-shelf LLMs underperform on specialized vocabulary, proprietary workflows, and domain-specific reasoning. VSP fine-tunes and optimizes models on your data to close the gap.
Foundation models are trained on general internet data. They do not know your taxonomy, your document formats, your compliance requirements, or your customer vocabulary. The gap between what a base model does and what your use case needs is exactly what fine-tuning closes.
VSP runs systematic fine-tuning experiments, builds rigorous evaluation frameworks, and optimizes models for both quality and inference cost before any model reaches production.
Adapt foundational models to your industry vocabulary, document formats, and reasoning patterns using your proprietary data.
Train models to follow your specific instructions, tone, format requirements, and refusal behaviors consistently.
Reduce model size and inference cost through quantization, pruning, and distillation without meaningful accuracy degradation.
Build rigorous evaluation frameworks to measure improvement, catch regressions, and surface failure modes before deployment.
Measure current model performance on your specific tasks to establish the improvement target.
Curate training data, run fine-tuning experiments, and validate against held-out domain benchmarks.
Quantize for production cost targets, implement serving infrastructure, and establish regression monitoring.
The difference between a useful AI product and a frustrating one often comes down to how well the model was adapted to the domain. VSP does that work.
