Blog

Cover recent releases (Nemotron ASR, Chatterbox, VibeVoice) and explain why a uniform voice layer approach with frequently swapping models is now realistic.

Y Combinator reports that 25% of its W25 project has codebases which are 95% AI-generated. This article discusses the real tradeoffs: speed vs. technical debt, prototype vs. production and why senior engineers report development hell.

Breaking down the real-world latency budget for voice AI agents (STT LLM TTS), explaining where milliseconds are lost and how streaming architectures and provider selection impact end-to-end response time.

Explore the trade-offs of locking into a single speech provider vs. using an abstraction layer for STT and TTS, learn about cost optimization, quality fallback, latency routing, and avoiding vendor lock-in

Stop using call recordings as an afterthought - here's how to build a voice-dating pipeline that scales, stays compliant, and actually makes your recordings useful.

Deep dive into how AI voice agents handle appointment scheduling from the moment a call comes in to sending the confirmation text, understanding the voice pipeline, calendar integrations and real-time processing.

Deep dive into the WebSocket Architecture patterns that make real-time voice AI work in production: from the connection life cycle to handling slow clients... and here's what nobody tells you.

PydanticAI is fantastic for building type-safe AI agents, but what if you want your agent to speak... Let's add real-time voice capabilities to your existing PydanticAI agent using the Sayna Python SDK.

Testing voice systems used to mean hiring real people to make test calls, but what if you could simulate thousands of realistic callers without the overhead, delay and cost of human testers?

Real conversations are messy . Users don't wait for your AI to finish talking before jumping into questions or corrections. Here's how to handle interruptions gracefully in Voice AI systems.

You have one server. You have fifty customers. Every customer wants their own phone numbers, their own webhooks, their own isolated traffic. Here is how Sayna routes SIP calls to the right tenant without duplicating infrastructure.

Adding voice to your AI agent isn't as hard as you think, but only if you stop trying to solve it alone.

The entire voice AI industry is in collective denial about latency. Here's the brutal math: cross 300ms and you're not having a conversation, you're operating a voice-activated vending machine.

The voice AI industry is collectively making users wait for no damn reason. Here's why streaming text chunks to TTS isn't just an optimization it's the difference between a conversation and a monologue.

While everyone's racing to make their AI talk faster, they're missing the goldmine flowing through their voice pipelines. Every conversation is leaking intelligence, and most companies are letting it drain straight into /dev/null.

The industry got it wrong again. Here's why separating voice streaming from agent logic isn't just smart architecture it's the only sane way forward.

Everyone's obsessed with which protocol is 'better' while missing the point entirely. WebRTC vs SIP isn't about technical superiority. It's about understanding what you're actually building and picking the tool that won't sabotage you six months from now.

The voice AI industry wants you to marry a provider. I say play the field. Here's why provider abstraction isn't just smart architecture it's your insurance policy against the inevitable enshittification of your favorite API.

Everyone's obsessed with LLMs and fancy prompts. Meanwhile, the real magic making voice AI feel human happens in 50 milliseconds of signal processing that nobody talks about.

The healthcare industry wants voice AI desperately. But HIPAA compliance isn't just a checkbox it's an architectural philosophy that changes everything. Here's how to build voice AI that lawyers actually approve and patients actually trust.

Everyone can build a voice AI demo. Ship it to production? Different story. Scale it to enterprise? That's where the bodies are buried. Here's the roadmap that actually works, complete with the scars to prove it.

Everyone wants to revolutionize voice communication. Meanwhile, 90% of the world's phone calls still run through infrastructure from the 1990s. Here's how to make peace with that reality and actually ship something that works.

The voice AI industry is collectively hemorrhaging money on a pricing model designed by people who've never run a business. Here's why bundled pricing is a scam and how separated architecture changes everything.

Everyone's fighting over whether LangChain beats PydanticAI or if raw prompts are the way. Meanwhile, your voice infrastructure is sitting there coupled to your framework choice like it's 2003 and we're still debating Struts vs Spring. Here's why that's insane.