Production-grade artificial intelligence.
We design, train, and deploy custom Large Language Models, RAG pipelines, and neural network image analysis systems. From clinical healthcare AI to enterprise automation, we build AI systems that are accurate, secure, and built for production.
Our AI capabilities
Custom LLM Training
Fine-tuning open-source models (Llama, Mistral) on your proprietary data to create private, secure language models tailored to your industry's terminology and compliance requirements.
RAG Pipelines
Retrieval-Augmented Generation systems that ground AI responses in your verified documents — reducing hallucinations with vector databases and semantic search.
Neural Network Image Training
Model training for medical imagery, including dental X-rays and clinical scans, with high-precision anomaly detection and classification — validated against clinician-verified data.
Autonomous AI Agents
Multi-agent systems that execute complex workflows, integrate with your APIs, and operate reliably in production — built on frameworks such as LangChain.
MLOps & Deployment
End-to-end ML pipelines, model registries, A/B inference, observability, and autoscaling for high-compute AI workloads.
AI Chatbots & Copilots
Conversational AI for customer support, internal knowledge bases, and domain-specific copilots — built on GPT-4, Claude, or fully private models.
Built on proven, enterprise-grade foundations.
AI questions, answered.
What is the difference between fine-tuning an LLM and building a RAG pipeline?
Fine-tuning teaches a model your domain knowledge by training it on your data — best for tone, terminology, and specialized behavior. RAG (Retrieval-Augmented Generation) connects a model to your documents at query time, so answers are grounded in verified, up-to-date information. Many production systems combine both; we recommend the right mix during discovery.
Can you build AI systems on our private or sensitive data?
Yes. Every engagement is covered by an NDA. Where data sensitivity requires it, we fine-tune and deploy models entirely within your own cloud environment (VPC or on-premise), so your data never leaves your infrastructure.
Which AI models and providers do you work with?
We work with commercial APIs such as OpenAI and Anthropic, and open-source models including Llama and Mistral. The right choice depends on your use case, budget, and data-sensitivity requirements — we provide a clear recommendation with trade-offs during the discovery phase.
How do you validate the accuracy of a custom AI model?
We establish accuracy targets during discovery, then validate models against held-out test datasets before production deployment. For medical imaging projects, validation is run against clinician-verified ground-truth data with documented performance reports.
How long does a custom AI development project take?
A typical engagement runs 8–14 weeks from discovery through production deployment. Smaller proof-of-concept projects can be delivered in 3–4 weeks, while ongoing model improvement continues after launch.
Let's turn your AI use case into a working system.
Reach out to discuss your data and objectives. We'll respond within 48 hours with an initial feasibility assessment, a proposed architecture, and recommended next steps.