Custom LLM Fine-Tuning ServicesOutperform GPT-4 in Your Domain
Train custom AI models on YOUR data with 100% privacy. Create uncensored, domain-specific models that deliver superior performance, unlimited usage, and complete data sovereignty.
What is LLM Fine-Tuning?
Transform general-purpose AI models into domain experts trained specifically on your business data
Generic AI Models
Current limitations
Fine-Tuned Models
Your competitive advantage
The Bottom Line
Why Fine-Tune Your Own LLM?
Build sustainable competitive advantages that can't be replicated
Proprietary Technology
Create unique AI models that competitors cannot replicate. Your custom-trained model becomes your competitive moat and intellectual property.
Superior Performance
Smaller fine-tuned models often outperform GPT-4 in specific domains. Achieve 95%+ accuracy on your specialized tasks.
Massive Cost Savings
Eliminate $50,000-$500,000/year in API costs. Pay once for fine-tuning, enjoy unlimited usage forever with zero per-token fees.
100% Data Privacy
Training happens on YOUR servers. Your proprietary data, customer information, and trade secrets never leave your infrastructure.
Uncensored Models
No corporate censorship or bias. Get honest, unfiltered answers to controversial questions. Perfect for medical, legal, and research applications.
Faster & Offline
Run locally with 10x faster response times. Works completely offline—no internet required. Perfect for edge deployment and remote locations.
Industry-Specific Fine-Tuning
Tailored AI models for your industry's unique requirements
Healthcare & Medical
HIPAA, GDPR compliant
Use Cases:
- ✓Clinical documentation and diagnosis assistance
- ✓Medical terminology and ICD-10 coding
- ✓Drug interaction analysis and prescription optimization
- ✓Patient history summarization
- ✓Uncensored medical advice without liability concerns
SUCCESS STORY
A multi-specialty hospital fine-tuned a model on 50,000 patient records, achieving 94% accuracy in diagnosis suggestions—saving doctors 3 hours per day.
Financial Services & Banking
RBI, SEBI, SOC 2 compliant
Use Cases:
- ✓Fraud detection and risk assessment
- ✓Investment analysis and portfolio optimization
- ✓Regulatory compliance (RBI, SEBI, Basel III)
- ✓Credit scoring and loan approval
- ✓Market sentiment analysis from financial reports
SUCCESS STORY
A private bank reduced fraud detection time by 85% using a fine-tuned model trained on 10 years of transaction data.
Manufacturing & Quality Control
ISO 9001, Six Sigma compliant
Use Cases:
- ✓Defect detection from images and sensor data
- ✓Predictive maintenance and equipment optimization
- ✓Supply chain optimization and demand forecasting
- ✓Process documentation and SOP generation
- ✓Quality assurance automation
SUCCESS STORY
An automotive manufacturer achieved 99.2% defect detection accuracy, reducing recalls by 67% and saving $2.3M annually.
Legal & Compliance
Attorney-client privilege maintained
Use Cases:
- ✓Contract analysis and due diligence
- ✓Legal research and case law search
- ✓Compliance monitoring and regulatory updates
- ✓Document summarization and brief generation
- ✓Uncensored legal opinions and risk assessment
SUCCESS STORY
A law firm reduced contract review time from 4 hours to 15 minutes using a model fine-tuned on 100,000 legal documents.
Education & Research
FERPA, data privacy compliant
Use Cases:
- ✓Personalized tutoring in regional languages (Hindi, Bengali, Odia)
- ✓Research paper summarization and literature review
- ✓Automatic grading and feedback generation
- ✓Curriculum development and lesson planning
- ✓Student performance prediction
SUCCESS STORY
A university created a multilingual AI tutor supporting Hindi, Bengali, and Odia—improving student engagement by 78%.
Government & Public Sector
Data sovereignty, national security compliant
Use Cases:
- ✓Citizen service automation (RTI, grievances)
- ✓Policy analysis and impact assessment
- ✓Multilingual support (22 official Indian languages)
- ✓Document processing and digitization
- ✓Emergency response optimization
SUCCESS STORY
A state government automated 80% of RTI responses, reducing backlog from 6 months to 3 days while supporting 5 local languages.
Our Fine-Tuning Process
From data preparation to deployment in 4-8 weeks
Discovery & Assessment
Week 1We analyze your use case, data sources, and success metrics. Define clear objectives and KPIs for the fine-tuned model.
DELIVERABLES:
Model Selection & Architecture
Week 1-2Choose the optimal base model (LLaMA, Mistral, GPT-based) and design the training architecture based on your compute resources.
DELIVERABLES:
Dataset Preparation & Curation
Week 2-3Transform your raw data into high-quality training datasets. Clean, format, and augment data for optimal training results.
DELIVERABLES:
Fine-Tuning & Optimization
Week 3-5Train the model with multiple iterations. Optimize hyperparameters, learning rates, and training steps for best performance.
DELIVERABLES:
Testing & Validation
Week 5-6Rigorous testing against real-world scenarios. Validate accuracy, safety, and performance across edge cases.
DELIVERABLES:
Deployment & Integration
Week 6-8Deploy the model on your infrastructure. Integrate with existing systems, APIs, and workflows.
DELIVERABLES:
Training & Support
Week 8 + 3 monthsTrain your team on using and maintaining the model. Provide ongoing optimization and support.
DELIVERABLES:
Investment & Pricing
Transparent pricing with massive ROI—typically pays for itself in 3-6 months
Starter
Single model fine-tuning for focused use cases
- ✓Single base model (up to 20B parameters)
- ✓5,000-10,000 training examples
- ✓Basic dataset preparation
- ✓2 training iterations
- ✓Standard optimization
- ✓On-premises deployment
- ✓30-day post-deployment support
- ✓Technical documentation
Professional
Advanced fine-tuning with optimization
- ✓Single model (up to 70B parameters)
- ✓10,000-50,000 training examples
- ✓Advanced dataset curation
- ✓4 training iterations
- ✓Advanced hyperparameter tuning
- ✓Multi-GPU deployment optimization
- ✓60-day post-deployment support
- ✓Team training (up to 10 people)
- ✓Custom API integration
Enterprise
Multiple models with ongoing optimization
- ✓Multiple models or ensemble systems
- ✓50,000+ training examples
- ✓Custom data pipeline development
- ✓Unlimited training iterations
- ✓Continuous model optimization
- ✓Multi-region deployment
- ✓90-day premium support
- ✓Dedicated solutions architect
- ✓Quarterly model updates
- ✓SLA guarantees
💰 ROI Analysis: Fine-Tuning vs. API Costs
❌Using GPT-4 API (Annual Cost)
✓Fine-Tuned Model (Total Cost)
After Year 1: Save $50,000+ annually in perpetuity
3-Year Savings: $95,000+ | 5-Year Savings: $195,000+
Frequently Asked Questions
What is LLM fine-tuning and why do I need it?
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What is LLM fine-tuning and why do I need it?
LLM fine-tuning is the process of training an existing AI model on your specific data to create a proprietary model optimized for your domain. It allows smaller models to outperform GPT-4 in your specific use case while maintaining complete data privacy and eliminating per-token costs.
How long does the fine-tuning process take?
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How long does the fine-tuning process take?
A standard fine-tuning project takes 4-8 weeks from data preparation to deployment. This includes dataset curation, training iterations, optimization, testing, and deployment on your infrastructure.
What makes your fine-tuning service different?
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What makes your fine-tuning service different?
We specialize in privacy-first, on-premises deployments. Your training data never leaves your servers, models are uncensored, and you gain unlimited usage with zero per-token fees. We focus on creating sustainable competitive advantages through proprietary AI models.
Can the fine-tuned model run offline?
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Can the fine-tuned model run offline?
Yes! All our fine-tuned models are deployed on your infrastructure and can run completely offline. This ensures data sovereignty, eliminates dependency on external APIs, and provides unlimited usage without internet connectivity.
What size datasets do I need for fine-tuning?
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What size datasets do I need for fine-tuning?
Effective fine-tuning can start with as few as 500 high-quality examples, though 5,000-50,000 examples typically produce optimal results. We help you curate and prepare datasets from your existing documents, conversations, and domain knowledge.
Will the fine-tuned model really outperform GPT-4?
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Will the fine-tuned model really outperform GPT-4?
In domain-specific tasks, yes! A 7B parameter model fine-tuned on your specialized data often achieves 95%+ accuracy compared to GPT-4's 70-80% on the same tasks. However, for general knowledge, GPT-4 remains superior.
What are "uncensored" models?
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What are "uncensored" models?
Uncensored models don't have corporate-imposed content filters. They provide honest, unbiased answers to controversial questions—critical for medical diagnosis, legal analysis, and research where censorship can be harmful.
What hardware do I need to run fine-tuned models?
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What hardware do I need to run fine-tuned models?
For a 7B parameter model: 1x NVIDIA A100 (40GB) or 2x RTX 4090. For 70B models: 4-8x A100. We help you select optimal hardware based on your budget and performance needs. Cloud and on-premises options available.
Can you fine-tune models in Indian languages?
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Can you fine-tune models in Indian languages?
Absolutely! We specialize in multilingual fine-tuning including Hindi, Bengali, Odia, Tamil, Telugu, and all 22 official Indian languages. Perfect for government, education, and customer service applications.
What ongoing costs are there after deployment?
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What ongoing costs are there after deployment?
Zero per-token costs! You only pay for compute infrastructure (which you already own or rent). Optional: quarterly optimization updates ($5,000-$15,000) to improve performance as you collect more data.
Ready to Build Your Proprietary AI Model?
Join leading organizations building competitive advantages through custom AI. Schedule a free consultation to discuss your fine-tuning project.
🔒 Free consultation. No commitment required. Your data remains confidential.