Systems that work together. AI you can govern.

It means AI that acts, not just responds. When we say agent-enabled, we mean Claude is embedded in your operational workflows - connected to your systems, configured to your business rules, and handling routine tasks end-to-end. For example, a Claude-based intake agent reads incoming referral documents, extracts key clinical fields, checks for missing information, and routes to the right care coordinator - with human review and escalation configured where your policies call for it. That's what agent-enabled means: not a chatbot, not a tab someone opens, not copy-pasting into Claude.ai. A workflow that handles routine work on its own, inside boundaries you define.

Both are available. Managed Intelligence gives your team governed access to Claude and ChatGPT - the tools they already know, under your policies, with configured context and usage reporting for the access we manage. When we build agents for specific workflows, we typically work with Claude because it's controllable and consistent in production - but day-to-day AI access isn't limited to one model.

Your team can keep familiar Claude and ChatGPT tools, with managed access, clearer data rules, relevant instructions and approved knowledge, and visibility into adoption of those tools. When there is a repeatable workflow worth automating, we can take that next step.

It can be, when implemented correctly - and the word correctly is doing a lot of work there. AI deployed carelessly in a healthcare environment creates real compliance and liability exposure. AI deployed with proper data boundaries, access controls, audit logging, and processing constraints can operate safely within your existing compliance posture. Every agent we build for healthcare clients starts with a compliance architecture review. We define what data the agent touches, how it's processed, where it's stored, and what the audit trail looks like before any configuration begins. Compliance documentation is part of the deliverable.

More than you'd expect. AI agents degrade in ways that aren't always obvious. Prompt configurations that worked well in January may produce weaker outputs by June as your workflows evolve, edge cases accumulate, or the inputs the agent processes change in character. Our maintenance work includes monthly output quality reviews, prompt architecture refinement based on real performance data, configuration updates when your workflows or policies change, monitoring for anomalies or error clusters, and quarterly strategic reviews to identify expansion opportunities. Ongoing maintenance isn't just keeping the lights on - it's refining the system based on how it actually runs.

No. You can start with the problem that matters most - stable IT, governed AI access, a workflow that needs automation, or a system that needs building. We scope what fits, then connect the pieces if your needs grow.

Yes. We audit what exists, identify gaps, and build a migration path that minimizes disruption.

Both. Alongside the managed IT, automation, and compliance work we're better known for, we also build custom software: internal tools, customer-facing portals, workflow systems, and full production MVPs. Because the same team that builds it also has to run it, the software we ship tends to hold up under real conditions rather than needing to be rebuilt six months later.

It depends on what they're building and what data it touches. AI coding tools like Cursor, Claude, and Bolt make it remarkably fast to build working applications - and your team is right to explore them. The concern isn't the tools themselves; it's what happens when an internal tool built in an afternoon starts handling patient records, financial data, or client information without authentication, audit logging, input validation, or environment separation. These are the same gaps we find in any ungoverned application, just arriving faster and from people who aren't primarily developers. As part of our environment assessment, we identify AI-built tools operating in your environment and evaluate them against the same security and compliance standards we apply to everything else. If something needs hardening, we handle it. If something should be rebuilt properly, we scope that.