Enterprise Custom LLM Fine-Tuning
Train private Large Language Models on your proprietary corporate knowledge, industry jargon, and workflows. We deliver tailored LLMs with full weight ownership and air-gapped VPC hosting.
Own Your Weights, Safeguard Your IP, and Outperform Generic Models
Commercial LLM APIs charge perpetual subscription fees and can update or deprecate models without notice. BigIntend fine-tunes state-of-the-art open models (LLaMA 3.3, Mistral, DeepSeek-V3) on your proprietary datasets, creating an enduring intellectual property asset for your company.
Full Weight Handover
You own 100% of checkpoint weights, training datasets, and Docker containers.
Zero Third-Party Leaks
Host models inside your private AWS/GCP VPC or on-premises NVIDIA servers.
Up to 80% Cost Savings
Self-hosted quantized models drastically reduce high-volume API token costs.
Our Custom LLM Engineering Capabilities
Parameter-Efficient Fine-Tuning (PEFT)
Fine-tune multi-billion parameter models using LoRA, QLoRA, and FlashAttention-2 on high-throughput GPUs.
- Fast Adaptation with Minimal GPU Memory
- Modular Task-Specific Adapter Swapping
Direct Preference Optimization (DPO & RLHF)
Align model responses with your enterprise tone, accuracy benchmarks, and compliance policies.
- Human-in-the-Loop Feedback Loops
- Reinforcement Learning from AI Feedback (RLAIF)
Domain-Specific Pre-Training & Continual Learning
Inject millions of legal, medical, or industrial engineering tokens into base models without catastrophic forgetting.
- Custom Tokenizer Training
- Synthetic Curriculum Data Generation
Ultra-Low Latency vLLM Inference Serving
Deploy models with PagedAttention and continuous batching on vLLM and TensorRT-LLM for sub-200ms latency.
- High-Throughput Multi-GPU Serving
- Dynamic Request Batching & Prefix Caching
NeMo Guardrails & Hallucination Defense
Deterministic guardrails that filter out off-topic queries, PII disclosures, prompt injections, and competitor mentions.
- Real-Time Output Sanitization
- Semantic Safety Policy Enforcers
Model Quantization & Edge Deployment
Quantize 8B to 70B models to INT4/INT8 (GGUF, AWQ, EXL2) for cost-effective local and edge execution.
- Mac Studio, Jetson & Serverless Deployment
- Ollama & LocalAI Containerization
LLM Frameworks, Accelerators & Infrastructure
Our 5-Step Custom LLM Delivery Lifecycle
Data Curation & Synthetic Augmentation
We clean proprietary documents, format instruction pairs, and generate synthetic multi-turn training data.
Base Model Selection & Tokenizer Tuning
We benchmark open models against your task and train custom domain tokenizers.
GPU Distributed Fine-Tuning & DPO
We train LoRA/QLoRA adapters across multi-GPU clusters with continuous loss tracking.
Benchmarking & Adversarial Red-Teaming
We test output precision against golden evaluation benchmarks and red-team for safety.
Production Deployment & Quantized Serving
We deploy vLLM inference endpoints in your VPC with automated telemetry and drift logging.
Frequently Asked Questions
Build a Custom LLM
Discuss your domain data and model requirements with our Lead Foundation Model Engineers.