Custom LLM Fine-Tuning Services
Train custom AI models on YOUR data with 100% privacy. Create uncensored, domain-specific models that outperform GPT-4.
Why Fine-Tune Your Own LLM?
Build sustainable competitive advantages that cannot be replicated
Spending $50K-$500K/Year on GPT-4 API Calls with Zero Competitive Moat?
💥 The Pain:
Your product runs on GPT-4/Claude API: $0.01-$0.03/1K tokens = $5K-$50K/month for 10K users. Competitors use same API = ZERO differentiation. OpenAI/Anthropic can raise prices tomorrow (you have no leverage). Data sent to OpenAI = they improve their model, you get nothing. Content moderation blocks legitimate use cases (medical, legal, creative writing). API downtime = your product down. Token limits = constrained features.
✅ Our Solution:
Custom Fine-Tuned Model: Your Proprietary Advantage. Fine-tune Llama 3.1 70B or Qwen2.5 72B on YOUR data: customer conversations, domain knowledge, writing style. Self-hosted on your infrastructure (AWS/Azure GPU instances or on-premises). Unlimited usage: $2K-$8K/month fixed cost vs $50K+ variable API costs. 100% data privacy: training data never leaves your servers. Uncensored: no content moderation (unless you want it). Better accuracy: fine-tuned on your domain outperforms general GPT-4. Competitive moat: model trained on your data cannot be replicated by competitors.
Generic AI Outputs Require Heavy Manual Editing—Destroying Productivity Gains?
💥 The Pain:
GPT-4 generates generic responses: "As an AI language model..." disclaimers, verbose corporate-speak, lacks your brand voice. Sales reps spend 30 minutes editing AI-generated emails. Medical AI gives general advice (needs doctor to rewrite for accuracy). Legal AI misses jurisdiction-specific nuances (liability risk). Customer support AI gives generic answers (customers frustrated, escalate to humans). Content AI requires 2-3 revision rounds (editors spend more time fixing than writing from scratch).
✅ Our Solution:
Fine-Tuned Model Learns Your Style + Domain Expertise. Sales model: trained on top performers' emails → generates emails in their voice, 90% ready to send. Medical model: trained on medical literature + your hospital's protocols → accurate, compliant responses. Legal model: trained on your jurisdiction's case law → jurisdiction-specific advice. Support model: trained on resolved tickets → context-aware answers matching your tone. Content model: trained on published articles → brand voice, no editing needed. Result: 10x productivity (1 hour → 6 minutes), 95% ready-to-use outputs.
Data Privacy Compliance Blocking AI Adoption—Losing to AI-Native Competitors?
💥 The Pain:
HIPAA/SOC2/GDPR prohibits sending customer data to third-party APIs (OpenAI, Anthropic). Legal team blocks GPT-4 integration: "We can't send patient records to OpenAI." Competitors using on-premises AI gaining market share (you stuck without AI). Enterprise customers require on-premises deployment (you lose $1M+ deals). EU customers demand GDPR compliance (data cannot leave EU). Financial services require data residency (regulators block cloud APIs).
✅ Our Solution:
On-Premises Fine-Tuned LLM: 100% Data Sovereignty. Deploy fine-tuned Llama/Qwen on your infrastructure: AWS VPC, Azure private cloud, or on-premises GPU servers. Zero external API calls: all inference runs locally. HIPAA compliant: PHI never leaves your environment (BAA with cloud provider, not OpenAI). SOC2 compliant: audit logs, access controls, encryption at rest. GDPR compliant: EU data stays in EU region. Pass enterprise security questionnaires: "Where does data go?" → "Our private infrastructure." Result: unlock $10M+ enterprise deals requiring on-prem AI.
Content Moderation Blocking Legitimate Business Use Cases—Losing Revenue?
💥 The Pain:
OpenAI/Anthropic content policies block legitimate use cases: Medical content flagged as "dangerous" (cancer treatment discussions blocked). Legal content flagged (divorce, criminal defense advice rejected). Creative writing flagged (fiction with adult themes rejected). Political analysis censored (bias toward mainstream views). Historical research blocked (sensitive topics filtered). Competitors with uncensored models gaining market share. Users frustrated: "Why can't your AI discuss X?" (churn to alternatives).
✅ Our Solution:
Uncensored Fine-Tuned Model: You Control Content Policies. Fine-tune base model without safety filters (Llama, Qwen, Mistral). Implement custom content policies aligned with YOUR business needs (medical advice allowed, hate speech blocked). Domain-specific safety: medical model allows graphic medical discussions, general model blocks them. Compliance: your lawyers define acceptable use (not OpenAI's lawyers). Competitive advantage: offer features competitors cannot (medical chatbot, unrestricted creative writing, political analysis). User satisfaction: no arbitrary censorship (users get expected responses).
LLM Fine-Tuning Packages & Pricing
Transparent pricing for custom AI model development
Fine-Tuning Starter
Timeline: 4-6 weeks
- Single base model (Llama 3.1 8B/13B or Mistral 7B)
- LoRA fine-tuning (parameter-efficient)
- 5K-10K training examples (we help curate)
- Single-task optimization (chat, summarization, or classification)
- AWS/Azure GPU deployment setup
- Basic inference API (FastAPI/vLLM)
- Model evaluation metrics
- Documentation & model card
- 30 days post-deployment support
- Ideal for: MVPs, single-task AI, proof-of-concept
Production Fine-Tuning
Timeline: 6-8 weeks
- Advanced base models (Llama 3.1 70B, Qwen2.5 72B, Mixtral 8x7B)
- Full fine-tuning or advanced LoRA
- 20K-50K training examples (data curation included)
- Multi-task optimization (chat + RAG + function calling)
- Production inference infrastructure (autoscaling, load balancing)
- A/B testing framework (compare to GPT-4)
- Hallucination reduction techniques
- Model monitoring & drift detection
- RLHF (Reinforcement Learning from Human Feedback) optional
- Quantization (4-bit/8-bit for cost efficiency)
- Team training (2 days)
- 90 days support
- Ideal for: Production AI products, 10K-100K users
Enterprise Fine-Tuning
Timeline: 10-14 weeks
- Multiple specialized models (sales, support, medical, legal)
- Massive training datasets (100K+ examples, synthetic data generation)
- Advanced techniques (DPO, PPO, constitutional AI)
- Multi-modal fine-tuning (text + images)
- Enterprise GPU infrastructure (multi-region, HA)
- On-premises deployment option
- Compliance (HIPAA, SOC2, GDPR-ready)
- Advanced evaluation (human eval, automated testing)
- Model versioning & rollback
- Active learning pipeline (continuous improvement)
- Integration with existing systems
- SLA guarantees (99.9% uptime)
- Security hardening (encryption, access controls)
- Team training (1 week)
- 120 days support
- Ideal for: Enterprise AI, compliance needs, mission-critical
AI Transformation
Timeline: 16-24 weeks
- Complete AI platform (multi-model ecosystem)
- Proprietary base model development (optional)
- Multi-modal AI (text, image, code, audio)
- Automated fine-tuning pipeline
- Data flywheel (user feedback → model improvement)
- Multi-region global deployment
- Advanced RLHF with human labelers
- Red-teaming & adversarial testing
- Model interpretability & safety
- Compliance automation (SOC2 Type II, HIPAA)
- Cost optimization ($100K+ savings/year)
- Dedicated AI research team
- Custom evaluation benchmarks
- Patent & IP strategy support
- 24/7 monitoring & incident response
- Team training (2 weeks)
- 180 days support
- SLA + uptime guarantees
- Ideal for: AI-first companies, regulated industries, research orgs
Complete LLM Fine-Tuning Deliverables
Everything you need for production AI deployment
Technology Stack
Industry-leading AI models and techniques we use
Base Models for Fine-Tuning
Llama 3.1 (8B/70B/405B)
Strengths: Best overall, 128K context, multilingual, Apache 2.0 license
Use: General purpose, chat, reasoning
Qwen2.5 (7B/72B)
Strengths: Excellent for code, math, multilingual (Chinese/English)
Use: Code generation, technical docs, STEM
Mistral (7B)
Strengths: Fast inference, efficient, good for edge devices
Use: Lightweight applications, mobile
BioMistral / Med-PaLM
Strengths: Pre-trained on medical literature
Use: Healthcare, medical diagnosis
CodeLlama
Strengths: Pre-trained on code, 100K context
Use: Code completion, bug fixing
Fine-Tuning Methods
LoRA (Low-Rank Adaptation)
Parameter-efficient, 99% of params frozen
QLoRA (Quantized LoRA)
4-bit quantization + LoRA, ultra-efficient
Full Fine-Tuning
Update all parameters, maximum customization
RLHF (Reinforcement Learning from Human Feedback)
Align model to human preferences
DPO (Direct Preference Optimization)
Simpler than RLHF, same benefits
Frequently Asked Questions
Everything you need to know about LLM Fine-Tuning
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 enables smaller models to surpass GPT-4 performance in specialized tasks while maintaining complete data privacy and eliminating token-based costs.
How long does the fine-tuning process take?
A standard fine-tuning project takes 4-8 weeks from data preparation to deployment. This timeframe encompasses dataset curation, multiple training iterations, optimization, testing, and final deployment on your infrastructure.
What makes your fine-tuning service different?
We emphasize privacy-first, on-premises deployments where training data remains on your servers. Models are uncensored, offer unlimited usage with zero per-token fees, and create sustainable competitive advantages through proprietary AI.
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 external API dependency, and enables unlimited usage without internet connectivity.
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 assist in dataset curation from existing documents, conversations, and domain knowledge.
Will the fine-tuned model really outperform GPT-4?
For domain-specific tasks, yes. A 7B parameter model fine-tuned on specialized data often achieves 95%+ accuracy compared to GPT-4's 70-80%. However, GPT-4 remains superior for general knowledge applications.
What are "uncensored" models?
Uncensored models lack corporate content filters, providing honest, unfiltered answers to controversial questions. This proves critical for medical diagnosis, legal analysis, and research where censorship could be counterproductive.
What hardware do I need to run fine-tuned models?
For 7B parameter models: one NVIDIA A100 (40GB) or two RTX 4090s. For 70B models: 4-8x A100 GPUs. We recommend optimal hardware based on budget and performance requirements, with both cloud and on-premises options available.
Can you fine-tune models in Indian languages?
Yes! We specialize in multilingual fine-tuning including Hindi, Bengali, Odia, Tamil, Telugu, and all 22 official Indian languages. This is ideal for government, education, and customer service applications.
What ongoing costs are there after deployment?
Zero per-token costs! You only pay for compute infrastructure you already own or rent. Optional quarterly optimization updates ($5,000-$15,000) improve performance as you collect additional data.
Still have questions?
Schedule a free consultation to discuss your specific use case
📞 Talk to an ExpertReady to Build Your Proprietary AI Model?
Schedule a free consultation to discuss your fine-tuning project.