Why We Built Persistence
The problem we saw
In 2023, we ran a contact center for a mid-size insurance company. We tried every voice AI product on the market. They all failed in the same way: they sounded like robots, they constantly interrupted callers, and they couldn't handle the normal messiness of real phone conversations — background noise, mid-sentence pauses, accents, emotion. Our callers hated them. Our support team hated maintaining them. We ended up back to human agents for everything except the most rigid, scripted interactions.
The conviction that drove us to build
We believed the problem wasn't AI capability — GPT-4 was already remarkable. The problem was infrastructure. Every platform we tried was stitched together from commodity cloud APIs with architectural compromises that made low-latency, high-accuracy conversations impossible at the component level. We believed that if you built the voice AI stack from scratch — co-located inference, purpose-built turn-taking models, streaming everywhere — you could build something that felt genuinely natural. That belief became Persistence.
What "persistence" means to us
The name has two meanings. The first is technical: Persistence maintains conversational context across the full duration of a call, not just the last few turns. Long call memory, recall of earlier parts of the conversation, coherent goal-tracking — these are what make a voice AI agent feel intelligent rather than amnesiac. The second meaning is about the product philosophy: we persist in conversations that are hard. Noisy calls, emotionally difficult calls, complex multi-step transactions. We don't give up and transfer to a human every time something unexpected happens.
What we've built
Today Persistence processes over 5 million calls per month across healthcare, financial services, retail, and real estate. We've reduced call center staffing costs for our customers by an average of 45%. We've helped healthcare providers schedule millions of appointments and reduce no-show rates by over 35%. We've built the voice AI infrastructure that Bland AI and Retell AI don't have and can't easily replicate: direct carrier interconnects, on-device inference, neural turn-taking models, enterprise compliance — all in a platform that any developer can use on day one.
What's next
Voice AI is in the same place that text AI was in 2021 — impressive in demos, rough in production, and not yet trusted by most enterprises. We believe that will change completely within 24 months. The companies that figure out how to deploy voice AI reliably today will have a structural advantage by the time it becomes mainstream. We're building the platform that makes that possible. If you want to be on the right side of that transition, we'd love to show you what Persistence can do.
