Side-by-side comparison of AI visibility scores, market position, and capabilities
Public safety AI automating non-emergency 911 calls with 74% automation rate; $14M Series A from NEA serving 5M+ Americans and saving dispatchers 3 hours daily across 12+ agencies.
Aurelian is a public safety AI company automating non-emergency 911 call handling — deploying AI voice agents that take caller information, gather incident details, and resolve or route non-emergency police, fire, and EMS requests without requiring a human dispatcher's time, allowing dispatch centers to focus on genuine emergencies requiring immediate human judgment. Founded and backed by Y Combinator and NEA, Aurelian raised a $14 million Series A led by NEA in August 2025, serving nearly 5 million Americans across 12+ agencies and achieving 74% average call automation with 3 hours saved per dispatcher daily.\n\nAurelian's AI handles non-emergency calls — noise complaints, minor property damage, requests for police reports, abandoned vehicle reports, and other routine situations that don't require immediate emergency dispatch. The system gathers structured incident information through conversational AI, routes genuine emergencies to human dispatchers immediately, and allows non-emergency situations to be handled asynchronously. This addresses a critical problem: 911 call centers in the US receive millions of non-emergency calls annually, creating backlogs that delay responses to genuine emergencies.\n\nIn 2025, Aurelian competes in the public safety communications technology market with Motorola Solutions (the dominant dispatch technology provider), RapidSOS, and emerging AI public safety platforms for 911 center modernization. The 911 system infrastructure in the United States is chronically underfunded and understaffed — many dispatch centers run with 15-30% staffing shortfalls, making AI automation of non-emergency call volume a genuine operational necessity rather than an optional improvement. The NEA Series A investment validates the market opportunity and Aurelian's early traction. The 2025 strategy focuses on growing from the current 12+ agencies to 50+ agency deployments, demonstrating measurable response time improvements for emergencies, and building the evidence base needed for adoption by larger metropolitan dispatch centers.
$500M Series D at $11B valuation (Feb 2026) — largest voice AI funding round ever. $330M ARR; 1M+ developers using the API. Enterprise customers: Deutsche Telekom, Revolut, Meta, Salesforce. Voices in 32 languages; real-time cloning from 1 second of audio.
ElevenLabs was founded in 2022 by Piotr Dabkowski and Mati Staniszewski, two former Google and Palantir engineers who set out to break the language barrier using AI voice technology. The company specializes in AI-powered voice synthesis, cloning, and dubbing, enabling developers and enterprises to generate human-quality speech in over 30 languages. Its core technology combines deep learning models trained on massive speech datasets to produce natural-sounding voices indistinguishable from real humans.\n\nElevenLabs offers a suite of products including its flagship text-to-speech API, voice cloning tools, and an AI dubbing platform that localizes video content while preserving the speaker's original voice. Its products target a broad audience—from indie developers building audio apps to large enterprises deploying voice interfaces at scale. Key differentiators include ultra-low latency streaming synthesis, fine-grained voice customization, and a growing library of pre-built AI voices across accents and styles.\n\nElevenLabs has grown rapidly, surpassing $330M in annualized revenue and serving over 1 million developers. Enterprise clients include Deutsche Telekom, Spotify, and leading media companies. In February 2026, the company closed a $500M Series D at an $11B valuation, cementing its position as the market leader in AI voice. Its APIs power podcasts, audiobooks, video games, and customer service bots worldwide, making ElevenLabs the default infrastructure layer for AI-generated audio.
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