With ~1M hours of speech across 25 languages, Granary will advance multilingual ASR/AST models significantly. For research labs building better speech models, this is great. But here's the architectural reality: STT and TTS are merely I/O layers. The real complexity lives in the orchestration layer between them.This is where production systems catastrophically fail:
Context gets lost between turns.
Memory collapses after 30 seconds.
User intent is misread in a high-stakes flow.
Real-world cases aren’t simulated before you ship.
So let’s be clear: For research labs or model builders, Granary gives better inputs/outputs. But, for companies building voice AI agents → that’s not usable.
That’s why simulations and evaluations matter more than prettier voices. They’re the only way to know if your agent will actually survive a real 5-minute customer call. Because when a real Spanish customer calls, they don’t care how fancy your TTS sounds. They care if the agent understands them, remembers, and solves the problem.
And, no amount of training data fixes these architectural failures. The industry's obsession with dataset size creates a dangerous illusion - pristine training metrics that shatter on contact with production reality.
This is precisely where Future AGI's Simulate platform intervenes in the AI stack. While Granary provides the acoustic foundation, we stress-test the orchestration layer with:
Synthetic personas that systematically probe failure modes
People switching languages mid-sentence
Bad connections causing people to talk over each other
Angry or stressed callers that confuse your AI
Think GAN-style adversarial testing applied to conversational patterns: finding the exact combination of accent, noise, and contextual ambiguity that breaks your carefully trained models.
The EU AI Act doesn't evaluate training hours. It demands demonstrable reliability across demographics and edge cases. When a Spanish customer calls your voice agent, they judge whether the system maintains context and executes correctly despite real-world chaos not your WER metrics.
The real question isn't how much data you trained on. It's how many ways you tested it could fail.
👉 If you’re building voice AI: do you worry more about how your agent sounds or whether it can understand, remember, and act when it matters?
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