LLM Development & Fine-Tuning Services
Custom large language models fine-tuned on your proprietary data — 40% better domain accuracy than generic LLMs and 90% cost reduction at scale vs GPT-4. Private deployment in your own VPC.
LLM Development Services
Six LLM engineering capabilities — from fine-tuning to private deployment to model compression.
Supervised Fine-Tuning (SFT)
Fine-tune Llama 3, Mistral, Falcon, and Phi-3 models on your domain data — customer interactions, internal documents, code, and structured records — for task-specific accuracy that generic LLMs cannot match.
RLHF & Preference Optimization
Reinforcement Learning from Human Feedback and DPO (Direct Preference Optimization) to align model behavior with your specific quality and safety standards beyond what SFT alone achieves.
Custom LLM Architecture
When off-the-shelf architectures don't fit — specialized attention mechanisms, domain-specific tokenizers, reduced-parameter models optimized for edge deployment, and mixture-of-experts configurations.
Model Evaluation Frameworks
Domain-specific evaluation harnesses with automated benchmarks, adversarial test suites, and human evaluation pipelines. Track model performance continuously across fine-tuning iterations.
Private & On-Premise Deployment
Deploy fine-tuned models in your own VPC — AWS, Azure, or GCP — with NVIDIA A100/H100 inference optimization, quantization (GGUF/GPTQ), and vLLM for high-throughput serving.
Model Optimization & Compression
Quantization (4-bit, 8-bit), LoRA/QLoRA for parameter-efficient fine-tuning, knowledge distillation to smaller models, and ONNX export for latency-sensitive deployment targets.
Fine-Tuned LLM vs Generic GPT-4
For domain-specific applications at scale, fine-tuned open-source LLMs consistently win on accuracy, cost, and privacy.
How We Build Custom LLMs
From data audit to production-ready model in 3–8 weeks.
Data Strategy & Curation
We audit your available data — volume, quality, format diversity, and domain coverage. We establish minimum viability thresholds and build data cleaning, deduplication, and quality filtering pipelines.
Base Model Selection
Selection from Llama 3.1/3.2, Mistral, Phi-3, Gemma, and Falcon based on your parameter budget, deployment constraints, and task profile. We benchmark base models on your eval set before committing to fine-tuning.
Fine-Tuning Infrastructure
GPU cluster provisioning (A100/H100), distributed training setup with DeepSpeed or FSDP, checkpoint management, and experiment tracking with W&B or MLflow. QLoRA for cost-efficient adapter-based training.
Training & Iteration
Supervised fine-tuning with hyperparameter optimization, followed by optional RLHF/DPO alignment. Each training run is evaluated against your domain benchmarks — we iterate until targets are met.
Evaluation & Red Teaming
Comprehensive model evaluation: domain accuracy, instruction following, safety, bias, hallucination rate, and adversarial robustness. External red team testing for production readiness certification.
Deployment & Serving
Model quantization and optimization, vLLM or TGI serving infrastructure, load balancing, auto-scaling, and monitoring. OpenAI-compatible API endpoints for drop-in replacement in existing applications.
LLM Technology Stack
Is fine-tuning right for your use case?
Free 30-minute LLM strategy session — we'll assess your data, use case, and whether fine-tuning is genuinely the right investment.
Related Services
Frequently Asked Questions
Technical answers about LLM fine-tuning and custom model development from our team.
Build a Custom LLM That Knows Your Domain
Schedule a 30-minute strategy session with our LLM engineers. We'll assess your data, evaluate whether fine-tuning is right for your use case, and give you a realistic cost-benefit analysis.