Custom AI Model Training
AI Fine-Tuning
Fine-tune GPT, Llama, Mistral, and other AI models on your proprietary data. Get more accurate, brand-consistent, and cost-efficient AI outputs tailored to your specific business needs.
- Quick Answer
What is AI fine-tuning?
AI fine-tuning is the process of training a pre-existing AI model (like GPT) on your specific data to improve accuracy, relevance, and consistency for your use case.
- Who This Is For
- Teams getting inconsistent results from generic AI
- Companies needing AI that understands their domain
- Businesses with proprietary data for training
- Organizations wanting to reduce AI API costs
- SaaS companies building AI features into products
- Enterprises requiring brand-consistent AI outputs
- Problems We Solve
- Generic AI doesn't understand your industry terminology
- AI outputs require heavy editing to match brand voice
- High token costs from overly verbose AI responses
- Inconsistent quality from general-purpose models
- Sensitive data can't be sent to third-party AI APIs
- AI model can't handle domain-specific edge cases
What's Included
- GPT-4, GPT-3.5, Llama 3, and Mistral fine-tuning
- Training data preparation and curation
- Domain-specific knowledge embedding
- Brand voice and style training
- Evaluation and benchmarking against base models
- Cost optimization through smaller fine-tuned models
- Hosted model deployment (API endpoint)
- A/B testing fine-tuned vs base models
- Ongoing model retraining with new data
- Data privacy and compliance controls
Why Choose Mitash
ML Engineering Expertise
Our team has deep experience with transformer architectures, training pipelines, and model optimization.
Data-First Approach
We invest heavily in data quality — cleaning, curating, and structuring your training data for optimal results.
Production Deployment
We don’t just train models — we deploy them as production APIs with monitoring and auto-scaling.
Pricing & Packages
Starter Fine-Tune
$5,000
Fine-tune for a single use case
- Data preparation (up to 1K examples)
- Single model fine-tuning
- Evaluation report
- API endpoint deployment
- 30-day support
Most Popular
Business Fine-Tune
$12,000
Multi-use-case fine-tuning
- Data curation (up to 10K examples)
- Multiple model comparisons
- Brand voice calibration
- Advanced evaluation suite
- Hosted deployment
- Retraining pipeline
- 60-day support
Enterprise Training
$25,000+
Full-scale custom model training
- Unlimited training data
- Custom model architecture
- On-premise deployment option
- Auto-retraining pipeline
- Enterprise security
- Dedicated ML engineer
- SLA guarantee
What Our Clients Say
“Our fine-tuned model generates legal summaries with 94% accuracy — up from 71% with generic GPT. The investment paid for itself in a month.”
Andrew Mitchell
CTO, LegalTech Startup
“Fine-tuning reduced our AI API costs by 65% while improving output quality. Smaller, smarter models are the way forward.”
Rebecca Adams
VP Engineering, DataCorp
“Mitash fine-tuned a model on our 10 years of customer support data. It now handles 80% of tickets without human intervention.”
Mark Thompson
Head of Support, Enterprise SaaS
Ready to Get Started?
Frequently Asked Questions
Which AI models can be fine-tuned?
GPT-4, GPT-3.5 Turbo, Llama 3, Mistral, and most open-source LLMs. We recommend the best model based on your use case and budget.
How much training data do I need?
Minimum 100–500 high-quality examples for basic fine-tuning. For best results, 1,000–10,000 examples. We help prepare and curate data.
How long does fine-tuning take?
Data preparation takes 1–2 weeks. Training takes 1–3 days. Total project timeline is 3–6 weeks.
Will fine-tuning reduce my AI costs?
Yes. Fine-tuned smaller models often outperform larger generic models, reducing token costs by 50–70%.
Is my data safe during fine-tuning?
Yes. We use encrypted data pipelines and can work with on-premise or private cloud infrastructure for sensitive data.
What's the difference between fine-tuning and prompt engineering?
Prompt engineering optimizes instructions sent to a generic model. Fine-tuning modifies the model itself using your data. Fine-tuning produces more consistent, accurate results.
Can I fine-tune with proprietary data?
Yes. That’s the primary use case — training on your unique business data that generic models haven’t seen.
How do you measure fine-tuning success?
We benchmark against the base model using accuracy, relevance, consistency, and cost metrics on your specific evaluation dataset.
Can the model be updated with new data?
Yes. We set up retraining pipelines that periodically update the model with new examples.
Do I own the fine-tuned model?
For open-source models, yes — full ownership. For OpenAI fine-tunes, the model lives in your OpenAI account.
What if fine-tuning doesn't improve results?
We start with a feasibility assessment and small-scale test. If fine-tuning isn’t the right approach, we recommend alternatives like RAG or prompt engineering.
What is RAG vs fine-tuning?
RAG (Retrieval-Augmented Generation) retrieves relevant documents at query time. Fine-tuning trains the model on your data. Both have merits — we help you choose.
Can you fine-tune for multiple languages?
Yes. We fine-tune multilingual models for businesses operating across different language markets.
How do you handle model deployment?
We deploy via API endpoints on AWS, GCP, or Azure with auto-scaling, monitoring, and version management.
What industries benefit from fine-tuning?
Legal, healthcare, finance, customer support, content creation, eCommerce, and any domain with specialized language.
