Smallest AI
Real-time voice AI — TTS, STT, and voice agents built to scale.
Developer Tools·AI & Machine Learning·Other
About
Real-Time Voice AI Platform
Smallest AI is a real-time voice AI platform built on smaller, specialized models instead of one massive general-purpose system, delivering faster, more efficient performance for speech synthesis, transcription, and conversational voice agents.
Core Models
Lightning
Text-to-speech at ~100ms latency across 15+ languages, with instant voice cloning from just 5-15 seconds of audio.
Pulse
Speech-to-text with speaker and emotion detection across 38+ languages, plus built-in PII/PCI redaction.
Electron
A sub-3B parameter language model optimized for voice agents, with an OpenAI-compatible API and sub-300ms response time.
Hydra
One of the first native speech-to-speech models built for production, enabling full-duplex conversation without a cascaded pipeline.
Atoms: The No-Code Voice Agent Platform
Atoms lets teams create, test, and deploy production voice agents in minutes, with knowledge base grounding, outbound calling campaigns, and telephony in 40+ countries. A full conversational turn completes in under 800ms end to end.
Key Features
- Sub-800ms end-to-end response time for full agent turns
- No-code voice agent builder with knowledge base grounding
- Telephony in 40+ countries with outbound calling campaigns
- Mobile SDKs for iOS, Android, React Native, and Flutter
Smallest AI is SOC 2 Type II, HIPAA, GDPR, ISO 27001, and PCI-DSS compliant, with on-premise deployment for regulated industries like healthcare, finance, and debt collection.
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FounderPlaybooks.
What other founders did to grow.
2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
if I had not I would not take risks again right I mean okay one rejection boom I'm never going to get another client I'm never going to have anybody who's going to say yes right but I forgave myself for whatever that mistake was that I made in that process
Forgive yourself for past mistakes — that's what keeps you taking risks
Unforgiven failures compound into risk aversion. After one rejected pitch you stop pitching. After one botched launch you stop launching. The founders who keep shipping aren't lucky — they've practiced the specific muscle of forgiving themselves for the last failure so the next attempt costs less. Treat self-forgiveness as a tactical skill, not a therapy concept.
We have a percentage of the profits and a fixed monthly retainer. Since it's profit-based, he's incentivized to actually do what's best for the app — he's constantly thinking about what to do and how to make the greatest videos.
One profit-share influencer takes app from $300 to $35K MRR in one year
Flo structured his influencer deal around profit-sharing rather than flat fees or revenue percentage, which he found didn't scale well. Because the creator has real skin in the game, he acts more like a co-founder on growth than a contractor delivering videos.
There's a play for whatever you're stuck on.
Read all 2722 playbooks
Comments
1Hey 👋 built this so voice AI stops feeling like separate parts stitched together. Try Lightning first!