Model Fine-tuning

Transform general-purpose AI models into domain experts. We fine-tune foundation models on your proprietary data to achieve dramatically better accuracy, lower latency, and reduced costs — turning off-the-shelf LLMs into competitive advantages unique to your business.

Discuss Your Model
40%
Average Accuracy Improvement
60%
Inference Cost Reduction
100+
Models Fine-tuned
10x
Faster Than Training From Scratch

Our Fine-tuning Services

From data preparation to production deployment — we handle the entire model optimization pipeline

Training Data Curation

We clean, deduplicate, annotate, and structure your proprietary data into high-quality training datasets. Bad data in = bad model out, so we invest heavily in data quality with automated validation pipelines and human review.

LoRA & QLoRA Fine-tuning

Parameter-efficient fine-tuning that adapts models using a fraction of the compute. We use LoRA, QLoRA, and adapter techniques to fine-tune models on consumer GPUs while maintaining quality comparable to full fine-tuning.

Full Fine-tuning at Scale

For maximum performance, we run full parameter fine-tuning on distributed GPU clusters. Ideal for large enterprise datasets where you need the model to deeply internalize domain knowledge — legal, medical, financial, technical.

Model Evaluation & Benchmarking

Rigorous evaluation against your specific use cases — not just generic benchmarks. We build custom eval suites that test the exact scenarios your model will face in production, with A/B testing frameworks to measure real impact.

Production Deployment & Serving

We deploy fine-tuned models on optimized infrastructure — quantized for fast inference, containerized for scaling, with monitoring dashboards tracking accuracy, latency, and cost per request in real time.

Continuous Model Improvement

Models degrade over time as the world changes. We set up continuous training pipelines that automatically retrain your model on new data, run evaluation gates, and promote improved versions to production safely.

Why Fine-tune With Us

Deep ML engineering expertise combined with production deployment experience

GPU Infrastructure Expertise

Data Pipeline Engineering

Custom Evaluation Suites

Your Data Never Leaves You

Optimized Inference Speed

Measurable ROI Tracking

Legal Contract Analysis — 92% Accuracy vs 67% Out-of-Box

A corporate law firm needed to analyze thousands of contracts for specific risk clauses, obligations, and deadlines. GPT-4 out of the box achieved 67% accuracy on their specific contract types — too many false negatives for legal work.

We fine-tuned a model on 15,000 annotated contract sections covering their 8 most common contract types. The fine-tuned model achieved 92% accuracy with dramatically reduced hallucination. Lawyers now use it as a first-pass reviewer that highlights potential issues, cutting contract review time from 3 hours to 40 minutes per document.

Legal Contract Analysis — 92% Accuracy vs 67% Out-of-Box

Medical Report Summarization — Deployed Across 30 Clinics

A healthcare network needed to generate patient-friendly summaries from complex medical reports. Generic models produced summaries that were either too technical for patients or too vague for clinical accuracy. Both failure modes were unacceptable in healthcare.

We fine-tuned a model on 50,000 expert-written summaries, with separate evaluation by both physicians (for accuracy) and patients (for readability). The resulting model generates summaries at a 6th-grade reading level while maintaining medical accuracy validated by board-certified physicians. It's now deployed across 30 clinics, saving physicians 25 minutes per patient encounter.

Medical Report Summarization — Deployed Across 30 Clinics

E-commerce Product Classification — 60% Cost Reduction

An online marketplace with 2M+ products was spending $180K/year on API calls to classify products into their taxonomy of 3,000 categories. The generic model was accurate but expensive and slow at 800ms per classification.

We fine-tuned a smaller, specialized model (Llama 3.1 8B) on their product catalog. The fine-tuned model matched the accuracy of GPT-4 for their specific taxonomy while running 4x faster at 200ms per request. Hosting costs dropped from $15K/month to $6K/month — a 60% reduction with no quality loss.

E-commerce Product Classification — 60% Cost Reduction

Model Fine-tuning FAQ

We work with all major foundation models: Llama 3.x (Meta), Mistral/Mixtral, Gemma (Google), GPT-4o/GPT-4o-mini via OpenAI's fine-tuning API, Claude (via Anthropic partnerships), and open-source models on Hugging Face. We recommend the model based on your specific needs — smaller models for cost efficiency, larger ones for complex reasoning tasks.
It depends on the technique. For LoRA fine-tuning, you can see significant improvements with as few as 500-1,000 high-quality examples. For full fine-tuning, we recommend 5,000-50,000 examples. Quality matters far more than quantity — 1,000 expertly curated examples will outperform 100,000 noisy ones. We help you assess your data readiness during the discovery phase.
Absolutely. We can fine-tune entirely on your infrastructure (on-premise or your cloud account) so data never leaves your environment. For cloud-based training, we use encrypted pipelines and delete all training data after the model is delivered. We sign NDAs and can accommodate specific compliance requirements (HIPAA, SOC 2, GDPR).
Hallucination reduction is a core focus. We use techniques like DPO (Direct Preference Optimization) to penalize confabulation, build evaluation suites that specifically test for hallucination patterns, implement confidence thresholds so the model says "I don't know" when uncertain, and pair fine-tuned models with RAG systems for factual grounding. No model is hallucination-free, but we reduce it to acceptable levels for your use case.
Use fine-tuning when you need the model to learn a new skill, style, or domain expertise (like writing in your brand voice or understanding medical terminology). Use RAG when you need the model to access up-to-date factual information (like your product catalog or documentation). Many production systems use both — a fine-tuned model for reasoning combined with RAG for knowledge retrieval.
Data preparation and curation: $10-25K depending on volume and quality. LoRA fine-tuning with evaluation: $15-35K. Full fine-tuning with custom eval suites and production deployment: $40-80K. Timeline is typically 4-8 weeks from data handoff to production-ready model. We offer a $5K assessment phase where we evaluate your data, run baseline benchmarks, and estimate the improvement fine-tuning would achieve.

Ready to Make AI Work for Your Domain?

Let's evaluate your data and identify where fine-tuning can deliver the biggest performance gains. Start with a free assessment call.

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