u/DigitalSignage2024

Last year we had to make a call about our SaaS company. The default path was what most software companies are doing right now: add AI features around the edges. AI-generated templates, AI summaries, AI recommendations, AI content suggestions. The other path was much bigger: don't just make AI a feature. Make AI operate the software.

We run CastHub, a digital signage platform used in schools, gyms, retail stores, offices, and franchises. The traditional workflow was the same as every other SaaS dashboard. Log in, pick screens, pick content, set schedules, confirm changes, verify deployment. Works fine for one location. Breaks down when operations become urgent, repetitive, or spread across dozens of sites.

A school district gets a snow day call at 5:30 AM. A retail chain needs a Black Friday campaign live in 47 stores before opening. A franchise operator needs pricing updated across 23 locations immediately. Nobody wants to click through dashboards for that anymore. That was the point we realized adding "AI features" was solving the wrong problem. The opportunity was operational control.

Not "generate a better-looking promotion" but "show the snow day closure on every entrance screen across all 12 schools, replace morning content, revert tomorrow at 7 AM." Not "suggest a marketing campaign" but "push the Black Friday bundle to all 47 stores starting at each store's opening time, end it Sunday at close." Not "rewrite this menu copy" but "update smoothie pricing 8 percent across every cafe board, confirm before going live."

That shift changed how we thought about the company. We stopped thinking about AI as a feature layer. We started thinking about AI as an operational interface.

We chose Anthropic's Model Context Protocol because we didn't want to build per-vendor integrations for every AI assistant. MCP gives us one operational surface that works across Claude, ChatGPT, Microsoft Copilot, Cursor, and whatever comes next. Standardized tool definitions, built-in governance patterns. The public MCP server is at github.com/Cast-Hub/mcp-server if you want to see how the tools are structured.

The surprising part was that building the tools wasn't the hard problem. Governance was. AI is extremely confident when taking action. That's harmless when the task is "summarize this document." It's much less harmless when the task is "push this emergency message to every screen in a school district." We built the system around confirmation and accountability first. Every write action requires explicit user approval in chat before execution. Audit logs capture who requested what, when it happened, and what changed. Preview-before-confirmation and one-step rollback are next on the roadmap. The honest version: getting the safety story right takes longer than getting the tool definitions right, and we're not done with it.

The second thing we learned was that exposing more functionality doesn't automatically create a better experience. Our first instinct was "expose everything," which got us close to 50 tools. AI assistants get confused when there are too many indistinguishable options. We consolidated where granularity didn't help (mostly read operations) and kept fine-grained where it does (write operations with different confirmation flows). Ended up at 37 tools across 7 operational domains: presentations, devices, schedules, alerts, groups, plus a few auxiliary ones.

The third thing was probably the most important. Customers rarely talk about this as "AI." They talk about outcomes. "Which stores are still running last month's promo?" "Show this announcement on every entrance screen." "Restart the offline lobby display." "Put the storm closure message everywhere and undo it tomorrow." Nobody cares about the underlying architecture. They care that operational work becomes dramatically faster.

That changed how we think about SaaS products generally. The dashboard is still important, but it's not the only interface anymore. For repeatable operational tasks, conversation becomes the control layer.

Bigger picture, I think a lot of SaaS companies will face this same decision over the next few years. Add AI features around your product, or rebuild the product so AI can operate it directly. Those are very different bets. We took the second one.

We're CastHub. The tools documentation with input schemas is at cast-hub.com/mcp/tools.html if you want to see the operational surface in detail. Happy to answer technical questions about MCP, governance design, or the architecture decisions.