Train AI Models on Your Data. On Your Hardware. At the Cost of Electricity.
Off-the-shelf models don't know your industry. Fine-tuning creates AI that speaks your language, knows your products, and performs 10x better on your specific tasks.
Own Your AI. Completely.
Most AI deployments create new dependencies. Ours eliminate them. Here is what that means in practice.
Your Data Never Leaves
Training happens entirely on-premises — your facility, your hardware, your network perimeter. No data is uploaded to any cloud provider. Sensitive documents, proprietary knowledge, customer data: all of it stays inside your walls. Full stop.
No Per-Token Fees
API providers charge you every time your model runs. After fine-tuning, your model is yours — it runs at the cost of electricity and hardware depreciation. Heavy usage? Your costs are flat. You pay once to train, then run it as much as you want.
No Vendor Lock-In
You own the model weights outright. You own the training pipeline. You own the competitive advantage baked into that model. No vendor can revoke access, raise prices, or shut down. Your AI moat is yours — permanently.
Generic AI vs. Fine-Tuned AI
Understand the real difference before making a decision.
Generic AI
Trained on the internet — Wikipedia, books, Reddit, code repositories. Broad but shallow in any specific domain. It has never seen your company wiki, your pricing sheets, or your compliance documents.
- Trained on public internet data
- Knows nothing about your products
- Expensive per-token API billing
- Data sent to third-party servers
- Vendor controls availability & price
Fine-Tuned AI
Trained on your documents, your products, your industry. The difference is like hiring a generic assistant vs. a 10-year industry veteran who has memorized your entire company knowledge base.
- Trained on your proprietary data
- Speaks your domain language natively
- Runs on your hardware, flat cost
- Data never leaves your premises
- You own the weights permanently
“Generic AI = trained on the internet. Fine-tuned AI = trained on YOUR documents, YOUR products, YOUR industry.”
Technical Capabilities
State-of-the-art fine-tuning methods running on any hardware — Apple Silicon, NVIDIA GPUs, or cloud infrastructure.
Adaptation
LoRA / QLoRA / DoRA
Parameter-efficient fine-tuning methods that update a fraction of model weights, dramatically reducing memory footprint while preserving — and enhancing — model quality.
Reasoning
GRPO Training
Group Relative Policy Optimization training teaches models to reason step-by-step through complex problems. Build AI that thinks, not just predicts.
Compression
Knowledge Distillation
Transfer intelligence from large frontier models into compact, deployable ones. Run enterprise-grade AI on edge hardware without cloud dependency.
20+ Model Architectures
Your choice of foundation
Llama, Qwen, DeepSeek, Mistral, Gemma, and more. Pick the foundation model that fits your use case — we handle the fine-tuning.
Built on Our Own Open Source Framework
Every fine-tuning engagement runs on PMetal — our 18-crate Rust framework purpose-built for Apple Silicon. Native Metal GPU and Apple Neural Engine support. 180+ GitHub stars and growing. We eat our own cooking.
Who Fine-Tuning Is For
Any organization with proprietary knowledge, sensitive data, or high AI usage volume stands to benefit.
Domain-Specific Adaptation
Train on your internal documentation, SOPs, product catalogs, legal corpus, or industry knowledge base. The model learns your terminology, your standards, your world.
Private Training on Sensitive Data
Healthcare records, financial data, legal documents, trade secrets — data that can never touch a cloud API. On-premises training removes that constraint entirely.
Reasoning Improvement
Teach your model to reason through multi-step problems with GRPO training. Build AI that doesn't just look up answers — it works through them.
Model Compression for Edge
Distill large models down to lean, fast versions that run on laptops, embedded systems, or air-gapped environments without sacrificing accuracy on your specific tasks.
Custom Internal Assistants
Build a company-specific assistant that knows your HR policies, your product line, your engineering runbooks. A 10-year employee on day one.
Continuous Improvement
Fine-tuning is not a one-time event. As your business evolves, we retrain with new data. Your AI gets sharper over time — on your schedule, not a vendor roadmap.
Lower AI Operating Costs
Per-token API fees can run thousands per month at scale. On-premises inference on your own hardware costs single-digit dollars in electricity. Organizations that fine-tune with us reduce AI operating costs by up to 99% — while gaining complete data sovereignty.
Common Questions
Straight answers to what business buyers ask most.
- What hardware do we need?
- We support training on any hardware — Apple Silicon Macs, NVIDIA GPUs (CUDA), or cloud infrastructure. We specialize in Apple Silicon due to its exceptional cost-to-performance ratio with unified memory, but we'll work with whatever you have. During discovery we assess your existing hardware and recommend the most cost-effective path.
- How long does training take?
- A LoRA fine-tune on a 7B model with a modest dataset (50–100K examples) typically completes in hours. Larger models or richer datasets extend this to days, not weeks. Training time varies by hardware — Apple Silicon and high-end NVIDIA GPUs are fastest. We provide concrete time estimates after your data and target architecture are scoped.
- How much data do we need?
- Less than you think. High-quality, domain-specific fine-tuning can be highly effective with a few thousand well-curated examples. We help you structure and clean your existing documents, support tickets, product data, or knowledge bases into training-ready format.
- Is it really cheaper than API calls?
- Yes — significantly so at any meaningful usage volume. Running a fine-tuned 7B model on your own hardware costs roughly $0.0001–0.0005 per 1K tokens in electricity. GPT-4o-level API pricing is $2.50–$10 per 1M tokens. At 10M tokens/month you're spending $25–$100 in API fees vs. pennies in electricity. The hardware pays for itself within months regardless of platform.
Stop Renting AI. Start Owning It.
Schedule a fine-tuning strategy session. We'll assess your data, your hardware, your use case — and show you exactly what a custom-trained model would do for your business.
Schedule Your Fine-Tuning Session