Most AI voice platforms are elegant veneers over someone else’s infrastructure. Companies string together third-party APIs for telephony, speech recognition, and AI processing, then hope the seams don’t show when customers need to scale. The result is predictable: dropped calls, inconsistent performance, and finger-pointing when something breaks.
The problem they’re solving
Enterprise voice automation has been trapped in a cycle of compromise. Companies either settle for rigid, legacy call center software that can’t adapt to modern AI capabilities, or they build fragile systems by duct-taping multiple vendors together. Neither approach delivers the reliability enterprises need for mission-critical voice interactions at scale.
Fluents.ai takes a different approach: they own the entire stack. From telephony infrastructure to AI orchestration, they’ve built a unified platform that handles both inbound support and outbound campaigns without the integration headaches. Their system can deploy AI agents in under an hour through no-code tools, while still offering full API control for developers who need programmable voice capabilities.
The platform serves healthcare, finance, law, and SaaS verticals with AI agents that handle everything from qualifying leads and booking meetings to routing conversations and managing reception duties. One healthcare team reports saving $300,000 annually, while agencies are automating 90,000 daily calls through the system.
Why these founders
Florent de Goriainoff and Peter Nga bring complementary technical backgrounds to a problem that requires both deep infrastructure expertise and product intuition. The decision to build rather than integrate the core stack suggests founders who understand the technical complexity of voice AI and aren’t satisfied with surface-level solutions.
Their approach to verticalization across healthcare, finance, and legal sectors indicates an understanding of regulated industries where reliability and compliance aren’t optional features.
Why we’re watching
Voice AI platforms operating at enterprise scale face a maze of regulatory considerations that many founders underestimate. Healthcare deployments must navigate HIPAA compliance for voice data processing and storage. Financial services applications trigger additional scrutiny around data handling and customer communications. Legal sector integrations raise attorney-client privilege questions when AI systems process sensitive conversations.
The EU AI Act adds another layer of complexity for any platform processing voice data at scale, particularly around transparency requirements and risk categorization. Companies that own their full stack have more control over compliance implementations, but they also bear full responsibility for every component’s regulatory adherence.
As voice AI becomes standard infrastructure rather than experimental technology, the legal frameworks will likely evolve toward stricter data localization requirements and audit trails. Platforms built with compliance as an afterthought will find themselves retrofitting fundamental architecture decisions. Those that anticipate these requirements in their core design have a significant advantage.
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