AI Toolbox for Fire Protection: 4 Types, 4 Real Use-Cases

Jess: Welcome back to our AI series. I’m Jess, the VP of Product at Inspect Point, and I’m here with Pat, our CEO. Quick recap of our previous episode: we’ve already talked a little about AI, what it is, what it isn’t, and how you use it. There isn’t one AI. There’s a toolbox. Today we’ll show four kinds of AI and a real-world fire protection use case for each of them. With that, let’s jump into the individual types of AI.

Pat: Thanks for joining us to talk about how AI shows up in the real world.

Pat: Let’s start with Large Language Models, or LLMs. These are super-fast reader/writers you access via an app or API. They’re great for one-off analysis, summarizing long docs, and drafting clear language. Examples include ChatGPT, Google Gemini, Grok, and others.

Pat: For fire protection, one use case is AHJ Documentation Assistance. When preparing submittals or variance requests, LLMs can format the justification with code references, project details, and professional phrasing, reducing the back-and-forth with jurisdictions.

Jess: Another use case is Customer Communication Drafting. Generate professional emails, reminders, and deficiency notifications tailored to each client. The AI can adjust tone and technical depth depending on whether the audience is a building owner, AHJ, or facility manager.

Pat: For data, you’ll need project details, applicable codes and standards, and documentation requirements. The guardrail here is to use retrieval with citations, no unsourced claims. Redact PII and customer data. And always follow a “Draft, Review, Approve” workflow with edit tracking.

Jess: Next up: Niche AI Apps. These are standalone AI tools that do one thing really well, outside your core platform. They’re especially helpful when there’s a bottleneck you can isolate, like turning raw audio into structured notes. Examples include Otter, Fathom, and Midjourney.

Pat: For fire protection, you could record an internal meeting about Q3 projects, auto-transcribe, and extract to-dos and takeaways with a tool like Otter. Or use an AI Dialer to confirm and remind about upcoming appointments.

Jess: The guardrail: This can be sensitive, and you’ll need to consult state laws on recording. But if you get buy-in from the team, it can be a powerful tool to make sure nothing slips through the cracks after a meeting.

Jess: Third type: Embedded Assistants. These are AI features inside your core platform, like inspections, Microsoft Office, or Gmail. They improve speed and quality without clicking away. We’re obviously very partial to this one, but it really does speed up your daily activities dramatically because it works within your existing workflow to solve a specific pain point.

Pat: We launched our very own AI tool, Inspection Assistant, the first embedded assistant in fire protection. It runs while the tech is on site to check grammar, provide code guidance, detect anomalies, and generally QA the inspection report before it ever gets to the office. Microsoft Office also has an extensive set of embedded AI, from just chatting with Copilot to spinning up proposal decks in PowerPoint.

Jess: The guardrail: Keep humans in the loop to approve and create an audit trail. Assistants suggest; licensed techs sign off.

Pat: Speaking of assistants, let’s talk about Agents.

Jess: Agents are multi-step doers that follow checklists, click buttons, and move work across systems, like a reliable coordinator.

Pat: This area of AI is still a new frontier but will ultimately become the norm. Here’s an example: a Deficiency-to-Quote-to-Schedule Agent. When a deficiency is logged, the agent suggests severity, builds a quote from the price book, drafts the customer email, waits for approval, schedules the revisit with the right crew and skills window, and posts the work order.

Jess: The guardrail: Heavy testing will be required to ensure this runs smoothly, especially with human-gated steps at the start. You need a tamper-evident audit trail for every action.

Pat: Join us next episode where we dive into the most scalable, high-leverage component today: embedded AI. Thanks for joining us to talk about how AI shows up in the real world. In Episode 3, we’ll show you how embedded AI turns this into an engine you can measure and trust.