Applied AI
Enabled by foundations. Governed by design.
In our experience, AI initiatives are successful and create the most value when they're grounded in good data and designed into the solution, not bolted on afterward. We help organizations apply it where it actually makes sense, using the data platforms and governance already in place, so the results are explainable and something you can stand behind if someone asks how it works.
Where AI earns its place
We focus on applications with a clear owner, a measurable outcome, and a trustworthy data foundation underneath.
Conversational access to your data
Agents that let people ask questions of SQL and Graph data in plain language, so the answer to "where is this shipment" or "what does this record show" takes seconds, not a ticket in someone's queue.
Workflow automation
The unglamorous stuff: repetitive, error-prone back-office work that AI plus automation can just do. We paired AI with Playwright to automate lien-filing workflows across roughly ten western US states.
AI-assisted operations & security
Agents that analyze logged telemetry to surface what matters. We implemented Microsoft Sentinel security monitoring across global Power Platform environments, built on Azure AI Foundry and Copilot.
Value beyond the hype
The hardest part of AI was never the model. It's knowing where it actually belongs, trusting the data underneath it, and governing it so results are explainable and secure. That's enterprise architecture work, and it's where we start, not an afterthought once something's already in production.
That same discipline applies to choosing where AI belongs: define the problem first, then decide what actually solves it, sometimes that's AI, sometimes it isn't. In our experience, AI initiatives rarely fail on the model. They fail earlier, on a problem that was never clearly defined, or wasn't a real problem to begin with, just someone's science project wearing a business case.