Jane App Moves 800+ Knowledge Workers From AI Experiments to Production Workflows With Runlayer

Jane App is an all-in-one practice management platform for healthcare providers, supporting online scheduling, charting, telehealth, insurance, payments, and back-office operations. Named a Deloitte Technology Fast 50 Enterprise–Industry Leader five years running (2021–2025) and a Bronze Stevie® Award winner for Customer Service Department of the Year, Jane is trusted by over two hundred thousand providers across Canada.

Like many high-growth SaaS companies operating in the increasingly competitive healthcare vertical, Jane knew AI adoption would be a core force multiplier across its growing organization. So when Mark Hazlett joined the company as Chief AI Transformation Officer, his primary goal was to ensure every team was accelerating workflows (in some capacity) through automation.

At the time, Jane’s teams were primarily using ChatGPT and Claude for simple tasks, such as summarizing calls or generating briefs. Their hesitation to deepen workflow integrations with AI wasn’t technical overhead or operational inertia, but rather the unintentional leaking of PII and PHI that causes hundreds of large-scale incidents across the industry annually. “We’re a HIPAA-regulated healthcare company that frequently handles PHI,” Mark shares. “As much as we wanted to drive AI adoption, you can’t just let people connect arbitrary MCP servers to production systems without governance.”

Mark knew that with proper MCP governance, observability, and protections, security would swap from a roadblock to the core driver of AI adoption at Jane. He initially evaluated building an internal MCP control layer: a registry of approved servers, integration with Jane’s identity provider, tool-level authorization, and complete logs for every discovery and tool call.

However, that approach would pull product, platform, and AppSec engineers into maintaining the MCP infrastructure rather than improving Jane’s core product. Additionally, piecing together direct API integrations and custom MCP servers for each tool in Jane’s stack would have been slow, risky, and unscalable. He needed a centralized AI enablement platform to host and approve MCP servers, skills, and agents, while leveraging security and governance as growth drivers at every step.

After evaluating several alternatives, Mark discovered Runlayer through the AI and MCP community. Following a short proof of concept, he knew the platform could provide the MCP registry, identity-aware policies, and observability needed to drive AI transformation at Jane.

Jane chose Runlayer as its unified AI command-and-control plane, accelerating AI adoption while ensuring effortless security and alignment with HIPAA compliance.

Immediate Time-to-Value

The partnership started with a Runlayer-hosted onboarding call, during which the teams established core connectors across Google Drive, Calendar, Gmail, Atlassian, and Fellow on the same day. That baseline quickly expanded to encompass Slack, Notion, HubSpot, Jira, GitHub, and Canva, all united under Claude as Mark’s chosen LLM.

“One of the reasons we were so excited to work with Runlayer was their collaborativeness,” Mark shares. “They operate like an extension of our team, not just a vendor.” Within 10 days of the POC, the value was so clear that Jane had already committed to an annual contract.

Following integration, Runlayer became the backbone for how Jane governs, secures, and scales MCP-based AI workflows across the organization.

Centralized MCP Lifecycle and Governance

Runlayer enabled Jane to introduce new MCP connectors through a standard, company-wide workflow. When a knowledge worker requires a new connector, they submit a request in Slack; AppSec reviews the MCP, configures policies in Runlayer, and publishes it to a catalog where the approved groups can use it. This workflow replaces one-off, team-specific MCP setups with a governed inventory where every server is registered, reviewed, and auditable.

And to ensure only Runlayer-managed servers are active, Jane uses MCP Watch to effortlessly track shadow MCP usage across all knowledge worker devices. “We’ve taken a stance that we won’t allow any connectors that are not executed through Runlayer at Jane,” Mark explains. “It’s essential to ensure we maintain visibility as AI adoption grows.”

Governance That Enables Every Team

Knowing PHI isolation was table stakes, Jane used Runlayer’s policy engine to align AI access with HIPAA requirements, rather than relying on broad API keys. Now, Jane can enable Gmail, Calendar, and Drive for Claude while excluding specific Drive folders that contain PHI.

Previously blocked MCPs are enabled with scoped, tool-level permissions, making it safe for more teams to build and adopt MCP-based workflows without creating new blind spots.

That safety guarantee has directly supported AI adoption even across non-technical teams. For instance, Jane’s marketing team now accelerates SEO workflows across Notion, Google Search Console, and Ahrefs, and automates their site-update workflow end-to-end, enabling them to handle many more customer accounts than before.

For Jane, Runlayer’s combination of governance, visibility, and policy enforcement has become the foundation for AI adoption at scale, not just a safety layer on top.

With Runlayer, Jane transformed governance and observability for MCPs, Skills, and Agents from a roadblock to a continuous driver of AI adoption across its organization. Today, knowledge workers across engineering, security, and even non-technical teams continue to drive internal efficiencies with AI, without the looming worry of compliance shortcomings.