AI Assistant Eases 911 Non-Emergency Call Overload

When a salon owner’s complaint sparked an AI pivot

Aurelian’s origin story is a reminder that big public problems often show up in small, everyday ways. Founder Max Keenan started with appointment automation for hair salons. A blocked parking lot caused by a school carpool—and a 45-minute hold on a municipal non-emergency line—revealed a larger pain point: understaffed 911 call centers buried in non-urgent calls.

What Aurelian built and why cities are listening

Aurelian pivoted to an AI voice assistant that handles non-emergency calls—noise complaints, parking issues, lost or stolen items—so human dispatchers can focus on true emergencies. The system is trained to recognize signs of a real emergency and immediately transfer those calls to a human. For lower-priority incidents it collects key information and files reports or relays details for follow-up.

The approach is pragmatic: treat the call like a triage nurse at an ER. Does the incident need an immediate team on scene, or can it be documented and scheduled? Aurelian’s agent is already live in more than a dozen dispatch centers, including Snohomish County, Washington; Chattanooga, Tennessee; and Kalamazoo, Michigan.

Funding and competitive landscape

Aurelian raised a $14 million Series A led by NEA and says it’s handling thousands of real calls daily. Investors argue the startup isn’t replacing existing jobs but filling roles municipalities struggle to hire for. Competitors like Hyper and Prepared are building similar tools, but Aurelian emphasizes its real-world deployments as a differentiator.

Why this matters: dispatcher burnout and public safety

Dispatching is high-pressure work with high turnover. Many centers report overtime, 12- to 16-hour days, and staff shortages. Offloading routine calls can reduce stress, give telecommunicators breaks, and improve retention—while keeping emergency pathways intact.

Benefits cities should measure

• Reduced dispatcher workload and overtime costs
• Faster response for true emergencies
• Consistent data capture and audit trails for non-emergency incidents

Implementation caution and questions to ask

AI triage systems must be auditable, bias-tested, and tuned to local policies. Municipalities should ask: How fast does the system transfer suspected emergencies? What data is stored and who can access it? How are edge cases handled? Successful pilots include human-in-the-loop reviews and clear escalation paths.

Aurelian’s funding and live deployments are a notable step: this is no longer a lab experiment. For cities and counties grappling with staffing and response times, AI voice triage is becoming a practical lever. QuarkyByte supports leaders with data-backed scenario modeling, risk assessment, and pilot design so they can adopt these tools without compromising public safety.