Get your team and your data ready for AI.
Autonomous integrations, custom MCP servers, and hands-on training for the people who'll own AI inside your organization. Clean stack first. Autonomous everything next.
Your data infrastructure decides whether AI works or doesn't.
Rubi Works builds the data infrastructure, MCP servers, and autonomous workflows that make AI agents work in production. We assess your current stack, design governed AI access to your business systems, and train your team to own it. Most AI initiatives fail because the data isn't ready — disconnected systems, inconsistent schemas, manual handoffs that no automation can bridge. We fix the foundation first.
We build custom MCP servers that give AI agents direct, governed access to your business systems. We design autonomous integrations that handle the repetitive coordination work your team does today. And we train the power users who'll own and evolve these systems long after we're gone.
Our AI readiness work spans the full stack: data infrastructure assessment, integration architecture, agent design, and team enablement. Whether you're exploring what's possible or ready to ship your first autonomous workflow, we meet you where you are.
The gap between AI-ready and AI-late is widening every quarter.
The companies that move first aren't just adopting tools — they're reshaping how their teams operate. Clean data, governed access, and trained people are the prerequisites. Everything else is a demo.
Common questions.
What is an MCP server and why does my business need one?
An MCP (Model Context Protocol) server gives AI agents governed, real-time access to your business systems — ERP, CRM, billing, project management. Without one, AI agents can only work with data you manually feed them. With one, they can query your live systems, take actions, and automate multi-step workflows autonomously.
How do I know if my organization is AI-ready?
AI readiness depends on three things: clean, connected data (no silos or inconsistent schemas), governed API access to your core systems, and trained people who understand how to manage AI tools. If your team spends time manually moving data between systems, your data infrastructure isn't ready for AI agents.
What platforms do you build AI agents for?
We build MCP servers and autonomous workflows for AlayaCare, Q360, Odoo, Invoiced, and custom platforms. Our agents use Claude, n8n, Power Automate, and custom Python depending on the workflow requirements.
How long does an AI readiness engagement take?
Assessment takes 2-3 weeks. Building MCP servers and initial autonomous workflows typically takes 4-8 weeks in two-week sprints. Team training runs concurrently with the build phase. Most clients have production AI workflows within 3 months.
Building AI on top of disconnected systems?
Tell us what you're trying to automate. We'll show you what needs to be fixed in the data layer before any agent can run reliably.