Specialized Domain-Specific Models Trained on Your Proprietary Data
Keep your company's intelligence internal and optimize costs. We fine-tune large language models and develop lightweight Small Language Models (SLMs) tailored to your industry's terminology, security requirements, and hardware constraints.
The Power of Domain-Specific Custom Models
While general LLMs are versatile, they often hallucinate, leak proprietary data to public servers, and require high latency and hosting costs. Custom fine-tuned models are smaller, faster, completely secure, and achieve superior accuracy on specific tasks.
Proprietary Model Fine-Tuning: Train models (Llama-3, Mistral, Qwen) on your internal codebases, product wikis, and customer interaction logs.
Small Language Models (SLMs): Deploy lightweight models (under 8B parameters) that run locally on low-cost hardware or in your secure cloud instance.
Dataset Curation & Synthetic Data Generation: We clean, structure, and mask your raw logs, converting them into high-quality training pairs.
Model Evaluation & Benchmarking: Rigorous testing against standard metrics (MMLU, HumanEval) and custom domain test suites to prevent regression.
Security, Compliance, & Infrastructure
Data Protection: Your training dataset never leaves your secure cloud environment (AWS VPC, Azure, or local server).
Deployment Flexibility: Run fine-tuned models serverless or host them via dedicated vLLM / Ollama instances.
Cost Reduction: SLMs reduce API token spend by up to 90% compared to paying for proprietary API calls.
Our Fine-Tuning Process
1.
Data Curation & Masking: We collect and clean your logs, anonymizing sensitive employee or customer data.
2.
Model Selection: Choose the optimal base model (e.g. Llama-3-8B, Mistral-7B) depending on hardware and latency needs.
3.
Training & LoRA Adapters: Execute fine-tuning runs using advanced techniques like QLoRA to keep training costs low.
4.
Evaluation & Safety Checks: Test the fine-tuned model against benchmarks and safety guardrails before deploying.
