AI & Intelligent Systems
We design and build AI systems that do real work inside your business: answering questions from your own documents, automating repetitive workflows, and connecting AI agents to the tools your team already uses. For organizations that cannot send data to outside AI providers, we deploy private models inside your own environment.
Production AI, not demos.
Who it is for: Teams that have tried an AI pilot that never reached production, and leaders who want AI working on their own data without losing control of it.
- ✓Document Q&A and knowledge assistants (RAG) on AWS Bedrock, Azure and GCP
- ✓AI agents connected to business tools through the Model Context Protocol (MCP)
- ✓Private, self-hosted large language models with role-based access
- ✓Workflow automation with human approval steps built in
- ✓Cost and usage tracking for AI workloads
- ✓AI readiness assessments and roadmaps
What you get.
Fewer failed pilots and a clear path from prototype to production
Your data stays inside environments you control
Measurable time saved on repetitive knowledge work
Proof, not promises.
AI agents working across 5 business tools.
A self-hosted, containerized multi-agent AI platform on Azure that lets AI agents work across the company's business tools through the Model Context Protocol.
120B-parameter model. Zero data leaves the building.
A self-hosted AI platform giving a 15-person engineering team secure access to 120-billion-parameter models, with no data leaving the controlled environment.
AI & Intelligent Systems: common questions.
What is RAG and why does it matter for my business?
Retrieval-augmented generation (RAG) lets an AI model answer questions using your own documents and data instead of only what it learned in training. It is the most practical way to get accurate, source-backed answers from AI on internal knowledge such as policies, contracts and support content.
Can we use AI without sending data to OpenAI or other providers?
Yes. We deploy private, self-hosted models inside your own cloud account or data center, so prompts and documents never leave your environment. We have delivered this for an engineering team using a 120-billion-parameter model.
How long does an AI project take?
A focused proof of concept usually takes two to six weeks. Production rollouts depend on integrations and data readiness, and we scope them after a one-week discovery phase.
Let's scope your transformation.
No commitment · Same-week availability