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Data Model Training
Custom AI model fine-tuning and training on your proprietary data. We fine-tune GPT-4o, Claude, Llama 3, and custom transformer models on your specific domain, tone, and knowledge base so your AI always responds like your brand expert — not a generic chatbot.
What's Included
- Dataset Curation & Preprocessing
- Instruction Dataset Creation
- GPT-4o Fine-Tuning (OpenAI API)
- Llama 3 / Mistral Fine-Tuning (LoRA / QLoRA)
- Claude Fine-Tuning (Anthropic API)
- RLHF & DPO Alignment
- Domain Benchmark Evaluation
- Hallucination Rate Testing
- Inference API Deployment
- Self-Hosted Model Deployment
Generic large language models like GPT-4 know everything — which means they know nothing about your business specifically. They will hallucinate your product specs, give generic answers where you need precise ones, and default to cautious language when your brand voice is bold. RAG systems help, but they have limits: they can only retrieve context that fits in a prompt window, they struggle with highly specialised jargon, and they cannot learn your brand's specific reasoning patterns. For truly domain-specific AI, you need a model trained on your data.
We run the full model fine-tuning pipeline: data curation and preprocessing, instruction dataset creation, supervised fine-tuning (SFT) on GPT-4o or open-source models via LoRA/QLoRA, RLHF alignment where brand tone and accuracy are critical, evaluation against domain-specific benchmarks, and deployment to a dedicated endpoint. The result is an AI model that reasons like your best subject-matter expert, stays strictly within your brand guidelines, and achieves 40–60% higher accuracy on domain tasks than a base model with RAG alone.
Exactly What You Get
Every Model Training project includes these specific deliverables. No vague scope, no hidden extras.
Dataset Curation & Cleaning
Your raw data (documents, transcripts, emails, knowledge base) cleaned, deduplicated, formatted into instruction pairs, and quality-scored.
Fine-Tuned Model
Production-ready fine-tuned model weights deployed to OpenAI, Hugging Face, or your own infrastructure. Full version history maintained.
Evaluation Report
Benchmark comparison between base model and fine-tuned model across domain-specific test cases. Accuracy, hallucination rate, and tone adherence metrics.
Inference API
REST API endpoint for your fine-tuned model, with rate limiting, authentication, latency monitoring, and cost tracking.
Training Pipeline Documentation
Complete documentation of the training process, hyperparameters, and dataset structure so you can retrain when your data grows.
Ongoing Retraining Plan
Recommended retraining schedule and triggers (e.g., retrain quarterly or when new product documentation is published).
Pricing Framework
Fine-tuning projects start at $3,500 for a focused GPT-4o fine-tune on a well-organised dataset. Full pipeline builds (data curation, cleaning, training, evaluation, deployment) for proprietary domain models range from $8,000 to $30,000 depending on dataset size and model complexity. Open-source self-hosted model fine-tuning typically runs $5,000–$15,000.
Our Process
How we deliver Model Training projects from kickoff to launch.
Data Audit
Assess your existing data sources, quality, volume, and suitability for fine-tuning. Identify gaps.
Dataset Build
Curate, clean, and format your data into instruction pairs. Typically 1,000–50,000 examples.
Fine-Tuning
Run supervised fine-tuning with hyperparameter optimisation. Multiple training runs evaluated.
Evaluation
Test the fine-tuned model against domain benchmarks and compare to base model performance.
Deploy
Deploy to production inference endpoint with monitoring. Handover documentation provided.
Real Results From Real Clients
Numbers from actual Model Training projects we have delivered.
General-purpose GPT-4 giving inaccurate answers about jurisdiction-specific employment law. Hallucination rate of 22% on legal domain queries. Not usable in a professional context.
Custom fine-tuned model trained on 40,000 curated legal Q&A pairs. Hallucination rate dropped to 2.1%. Domain accuracy improved 58%. Model deployed as the core of their legal research assistant product.
Needed AI to answer technical product questions for sales team. Generic model gave vague answers and could not handle proprietary product specifications.
Fine-tuned Llama 3 model on product documentation, technical specs, and sales call transcripts. Sales team query accuracy rate 94%. Average time to answer technical questions cut from 15 minutes to 8 seconds.
Technologies We Use
Frequently Asked Questions
Everything you need to know about our Model Training service.
When should I fine-tune instead of using RAG?
Use RAG when your knowledge base changes frequently, your context fits within the prompt window, and you need explainability (you can show which document was retrieved). Choose fine-tuning when you need the model to deeply understand domain-specific reasoning patterns, adopt a specific brand voice consistently, handle highly specialised jargon the base model does not know, or when RAG retrieval accuracy is insufficient. In practice, the best systems combine both: a fine-tuned model with RAG retrieval on top.
How much data do I need to fine-tune a model?
For GPT-4o fine-tuning via the OpenAI API, you need a minimum of 10 high-quality examples, but 100–1,000 examples produce meaningfully better results. For open-source model fine-tuning with LoRA, 1,000–10,000 examples typically produce strong domain adaptation. For a full SFT run on a domain task, we recommend 10,000–50,000 examples. We can help you synthesise training data from your existing documentation if your dataset is small.
Will the fine-tuned model ever give wrong answers?
Fine-tuning dramatically reduces but does not eliminate errors. Our evaluation process measures hallucination rate before deployment and we set a maximum acceptable error rate with you before the project starts. For safety-critical applications (medical, legal, financial), we build explicit retrieval grounding and human review checkpoints into the deployment architecture.
Can I self-host the fine-tuned model?
Yes. For open-source base models (Llama 3, Mistral, Falcon), we deliver the fine-tuned LoRA weights and full deployment guide for self-hosting on AWS, Azure, GCP, or dedicated GPU servers using vLLM or llama.cpp. This gives you full data privacy, no per-token API costs, and complete control over the model.
How long does fine-tuning take?
Dataset curation and preparation takes 1–2 weeks depending on your data quality. GPT-4o fine-tuning via the API typically runs in 2–4 hours once the dataset is ready. Open-source LoRA fine-tuning on a well-curated dataset takes 6–24 GPU-hours. End-to-end project timeline (audit to deployed endpoint) is typically 3–6 weeks.
Does the fine-tuned model improve over time?
Not automatically — AI models do not learn from production interactions unless you explicitly collect data and retrain. We recommend a quarterly retraining schedule for most domain models, triggered by new product documentation, customer feedback analysis, or when you notice accuracy drifting. We provide a retraining pipeline and documentation so you can run subsequent fine-tuning runs yourself or with minimal support.
Further Reading
In-depth guides related to Model Training.
Related Services
Ready to Get Started with Model Training?
Book a free consultation and let's discuss exactly what your project needs.