+34%Domain accuracy vs GPT-4o
Enterprise Software
Custom LLM that outperforms OpenAI
Fine-tuned Llama 70B on proprietary data for domain-specific excellence.
Fine-tuningLlamaOn-premise
Custom LLM Training
Your data. Your model. Your competitive moat.
The most powerful AI advantage is not using the same model as everyone else ; it is having a model that knows your business, your customers, and your domain better than any general-purpose AI ever will. We take best-in-class open-source foundation models, fine-tune them on your proprietary data, align them to your use cases, and deploy them on your own infrastructure. You own the weights, you control the data, and no one else ever sees it.
Trusted by teams backed by
Y CombinatorSequoiaa16zAccelIndex
+34%Domain accuracy vs GPT-4o
1.2%Hallucination rate
0Data egress (bytes)
128KContext window (tokens)
28Tokens / second inference
100%Infrastructure ownership
General Models Are Everyone's Model. Yours Is Yours Alone.
When you use OpenAI, Anthropic, or Google APIs, you are using the same model as every one of your competitors. A model trained on the internet knows nothing special about your products, your customers, or your domain. A custom LLM trained on your 10 years of customer conversations, your internal documentation, your sales calls, and your proprietary research is a fundamentally different competitive asset ; one your competitors cannot replicate.
From Open-Source Foundation to Production Deployment.
We run a rigorous five-phase build process: model selection, data preparation, fine-tuning, alignment, and deployment. Every phase is documented, tested, and benchmarked against your specific use cases before moving forward.
Phase 1 ; Model selection: choose the right foundation (Llama, Mistral, Qwen, Phi, Gemma) for your compute budget and use case
Phase 2 ; Data preparation: clean, structure, and format your proprietary datasets for training
Phase 3 ; Fine-tuning: QLoRA, LoRA, or full fine-tuning depending on dataset size and performance targets
Phase 4 ; Alignment: RLHF or DPO training to align model behaviour with your requirements and values
Phase 5 ; Deployment: quantise for local/edge (GGUF, GPTQ, AWQ) or run full precision on your server
Phase 6 ; Evaluation: continuous benchmarking, red-teaming, and improvement cycles
Air-Gapped. Zero Data Egress. Completely Yours.
Your proprietary data is your most valuable asset. We deploy your custom LLM in a completely self-contained environment on your own infrastructure ; on-premise servers, private cloud, or air-gapped environments for the highest security requirements. Your data never leaves your network during inference. No API calls to third-party providers. No usage data sent anywhere. Complete sovereignty over your AI capabilities.
On-premise, private cloud, or air-gapped deployment options
Docker containerised for easy management and updates
SSO and RBAC integration for enterprise access control
Full audit logging for regulatory compliance (HIPAA, SOC 2, GDPR)
Automated backup and disaster recovery
Model versioning and rollback capabilities
Questions from Professionals Like You
- We work with all leading open-source models: Llama 3.x (Meta), Mistral/Mixtral, Qwen 2.5, Phi-4 (Microsoft), Gemma 2 (Google), DeepSeek, and others. We recommend the best fit based on your performance requirements, compute budget, and use case.
- It depends on the model size and throughput requirements. A quantised 7B model runs well on a single modern GPU (RTX 4090 or similar). A 70B model typically needs 2-4 A100/H100 GPUs. We spec the infrastructure as part of the engagement and can source and configure it for you.
- Quality matters more than quantity. We have seen excellent results with as few as 5,000 high-quality examples. For most enterprise use cases, 50,000-500,000 examples produces a meaningfully superior model. We help you identify, clean, and structure the most valuable data you already have.
- A standard engagement from kickoff to production deployment takes 6-12 weeks depending on complexity. Data preparation is often the longest phase. Fine-tuning a 7B model takes hours; a 70B model takes 1-3 days on appropriate hardware.
- Yes. We build REST API endpoints, Slack bots, custom chat UIs, and integrations with your CRM, ERP, or internal tools. We also build RAG pipelines that connect your LLM to live data sources so it always answers with current information.
Real Results. Happy Clients.
See how we've helped businesses like yours achieve measurable growth.
1.2%Hallucination rate
Healthcare Provider
Medical AI with compliance-aware deployment
Air-gapped deployment with zero data egress for sensitive patient data.
HealthcareHIPAASecurity
128KContext window tokens
Legal Services Firm
Document analysis at scale
Custom model processing thousands of legal documents with high accuracy.
LegalRAGDocuments
Related solutions
Your model. Your moat. No one else can replicate it.
The companies that will dominate their categories in 5 years are the ones building proprietary AI on their own data today. That window is open - but not forever. Book a call and let's scope what a custom model would mean for your competitive position.