Enterprise LLM
Your own LLM, trained on your business, deployed in your perimeter
We select, fine-tune, and deploy large language models for enterprise workloads, domain-adapted on your proprietary data, evaluated against your real tasks, and served from infrastructure you control. Not a chatbot wrapper. A model capability your competitors can't buy.
Overview
Generic models know the internet. Yours should know your business.
Off-the-shelf LLMs fail on the tasks that matter most to enterprises: your terminology, your document formats, your compliance constraints, your edge cases. We close that gap with a structured adaptation pipeline, supervised fine-tuning on curated examples from your workflows, RLHF aligned to your quality bar, and retrieval grounding over your knowledge base. The result is a model that performs like a trained employee, deployed privately so your data never trains anyone else's model.
What's included
Model selection & benchmarking
We benchmark Claude, GPT, Llama, Mistral, and specialized models against your actual tasks, not public leaderboards, and recommend the optimal model-per-workload mix with cost projections.
Supervised fine-tuning & RLHF
Curated training datasets built from your historical data, expert demonstrations, and preference rankings, producing models aligned to your domain and your quality standards.
Private deployment architecture
VPC, on-premise, or dedicated-endpoint serving with autoscaling, token streaming, and failover. Your prompts and outputs never leave your security perimeter.
Evaluation infrastructure
Task-specific eval suites that measure accuracy, hallucination rate, latency, and cost on every model version, so upgrades are evidence-based, never vibes-based.
Guardrails & safety layer
Input/output filtering, PII redaction, jailbreak resistance testing, and human-escalation rules, tuned to your risk tolerance and regulatory context.
Cost & latency optimization
Model routing (small model first, large model on escalation), prompt caching, quantization, and batch inference, typically cutting serving costs 40–70%.
Developer experience
Simple API. Powerful results.
Integrate in minutes with our SDK. Full TypeScript support, comprehensive documentation, and live examples for every feature.
How it works
From setup to production
Task & data audit
We map the workloads you want the model to perform, audit available training data, and define the eval criteria that will determine success, before any training run.
Baseline benchmarking
Candidate models are benchmarked on your real tasks to establish baselines. Often the right answer is a smaller fine-tuned model, not the biggest API.
Adaptation & evaluation
Iterative fine-tuning cycles with eval gates at each step. You see accuracy curves on your tasks, not training-loss charts.
Private deployment
Production serving inside your perimeter with monitoring, guardrails, autoscaling, and rollback. Integration with your applications via API compatible with OpenAI/Anthropic SDKs.
Continuous improvement
Production feedback flows into the next training cycle. Drift detection triggers re-evaluation. The model compounds in capability as it sees more of your work.
FAQ
Common questions
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