Emergency preparedness thrives on accuracy, relevance, and adaptability. The ability to design exercises that reflect the world as it is—rather than outdated assumptions—is vital for effective training and planning. At Preppr, we’re exploring a feature that would bring real-time intelligence into the exercise design process, helping users create scenarios that are not just realistic but also informed by current events and conditions.

The idea stemmed from a recent conversation with an emergency management practitioner in Hawaii. During a demo, they highlighted an issue: Preppr had referenced outdated neighborhood names and addresses for areas affected by the Maui fire. It was a small detail, but one that raised a critical question:

How can we give Preppr the ability to access up-to-date, contextualized information when the base AI model doesn’t inherently have it?

The Vision for Real-Time Intelligence

The solution we’re exploring is integrating Open Source Intelligence (OSINT) into Preppr. OSINT involves collecting and analyzing publicly available information and for us, the goal is to allow users to enrich their exercises with live, relevant data through a guided, conversational workflow.

Here’s how it might work:

1. Seamless Access
   While designing an exercise, users would have the option to open a dedicated panel for real-time intelligence. From there, Preppr would guide them through defining what they need—whether it’s evacuation updates, infrastructure impacts, or other key details.

2. Targeted, Contextual Queries
   Instead of overwhelming users with unnecessary information, Preppr would ask targeted follow-up questions to refine the query. For example:

   • “What geographic area should we focus on?”

   • “What time period should we prioritize—recent updates or historical trends?”

   • “Are there specific groups or challenges you’d like highlighted, like tourists or misinformation campaigns?”

3. Clear and Actionable Results
   The retrieved intelligence would be displayed in a clear, categorized format:

   • Scenario Context: Insights to enhance the overall exercise narrative.

   • Inject Suggestions: Pre-filled injects based on real-world events and conditions.

4. User Control and Integration
   Users would review the results and decide how to use them:

   • Accept: Approve the data for direct integration into Preppr.

   • Edit: Tailor the information for specific needs.

   • Reject: Exclude irrelevant details.

The approved intelligence would then seamlessly enhance scenario descriptions, injects, or supporting materials, ensuring exercises remain relevant and actionable.

Why It Matters

Real-time intelligence integration is about closing the gap between planning and reality. Disasters evolve quickly, leaving yesterday’s data outdated. By enabling users to pull in only the information they need, Preppr empowers emergency managers to design exercises that reflect the complexities of modern emergencies.

This feature also reinforces our commitment to user-friendly design. By embedding intelligence retrieval directly into Preppr’s existing workflow, we ensure the process is intuitive and doesn’t disrupt the creative flow of scenario building.

Next Steps

This feature is still in the exploration phase. It’s one of the big ideas we’re prioritizing as we refine Preppr’s core functionality. The insights from practitioners like the one in Hawaii help us identify challenges and envision solutions that will genuinely improve the way exercises are designed.

By combining Preppr’s existing strengths with real-time intelligence, we’re not just improving the tool—we’re enhancing the preparedness capabilities of the people who use it.

We’re excited about where this can go and look forward to bringing it to life in the near future.