Enterprise voice AI splits into three architectures, shaping compliance
Enterprises that are rolling out voice‑driven assistants quickly learn that compliance isn’t just a matter of picking the most accurate language model. Regulators care about how audio is handled, what data stays on‑prem, and how quickly a system can respond under real‑time constraints. That’s why product teams are dissecting the underlying stack before they even look at headline performance numbers.
The choice between a pipeline that streams raw speech, one that transcribes first, or a hybrid that offloads processing to the cloud can shift the balance between latency, governance, and budget. Companies such as Google and OpenAI have already released offerings that keep the original acoustic cues intact, positioning them differently from solutions that rely on text‑only pipelines. Understanding these architectural trade‑offs is becoming the first step in mapping a compliance posture that satisfies both internal policy and external audit requirements.
The enterprise voice AI market has consolidated around three distinct architectures, each optimized for different trade‑offs between speed, control, and cost. S2S models — including Google's Gemini Live and OpenAI's Realtime API — process audio inputs natively to preserve paralinguistic signals li.
The enterprise voice AI market has consolidated around three distinct architectures, each optimized for different trade-offs between speed, control, and cost. S2S models -- including Google's Gemini Live and OpenAI's Realtime API -- process audio inputs natively to preserve paralinguistic signals like tone and hesitation. But contrary to popular belief, these aren't true end-to-end speech models.
They operate as what the industry calls "Half-Cascades": Audio understanding happens natively, but the model still performs text-based reasoning before synthesizing speech output. This hybrid approach achieves latency in the 200 to 300ms range, closely mimicking human response times where pauses beyond 200ms become perceptible and feel unnatural. The trade-off is that these intermediate reasoning steps remain opaque to enterprises, limiting auditability and policy enforcement.
These modular stacks follow a three-step relay: Speech-to-text engines like Deepgram's Nova-3 or AssemblyAI's Universal-Streaming transcribe audio into text, an LLM generates a response, and text-to-speech providers like ElevenLabs or Cartesia's Sonic synthesize the output. Each handoff introduces network transmission time plus processing overhead. While individual components have optimized their processing times to sub-300ms, the aggregate roundtrip latency frequently exceeds 500ms, triggering "barge-in" collisions where users interrupt because they assume the agent hasn't heard them.
Unified infrastructure represents the architectural counter-attack from modular vendors. Together AI physically co-locates STT (Whisper Turbo), LLM (Llama/Mixtral), and TTS models (Rime, Cartesia) on the same GPU clusters. Data moves between components via high-speed memory interconnects rather than the public internet, collapsing total latency to sub-500ms while retaining the modular separation that enterprises require for compliance.
Together AI benchmarks TTS latency at approximately 225ms using Mist v2, leaving sufficient headroom for transcription and reasoning within the 500ms budget that defines natural conversation.
Three architectures now dominate enterprise voice AI. One is native S2S, like Google's Gemini Live and OpenAI's Realtime API, preserving paralinguistic cues. Another is modular, offering granular control and audit trails.
A third, not detailed, balances speed and cost. Decision‑makers must weigh speed against compliance, a shift from pure performance to governance. The split reflects two forces reshaping the market, though the exact drivers remain vague.
Enterprises that prioritize emotional fidelity gravitate toward native stacks, while those needing auditability stick with modular pipelines. Cost considerations push some toward the middle option, yet the trade‑off calculations are still emerging. It's unclear whether any single architecture will become the default as regulations tighten.
Vendors continue to position their solutions within these segments, but the long‑term compliance posture of each remains to be proven. As voice agents move from pilots into regulated environments, the architectural choice may prove more consequential than model quality alone. The market’s evolution will likely be measured by how well each approach satisfies both speed and governance requirements.
Further Reading
More in Business & Startups
Sam Altman hires Head of Preparedness for AI risks, mental health, cybersecurity
Why does a tech leader need a “Head of Preparedness”? While the title sounds like a safety net for...
Groq, founded by ex‑Google exec Ross, to assist NVIDIA on inference chips
Ross Wintrobe’s departure from Google set off a modest but notable shift in the AI‑hardware niche.
OpenAI says prompt injection persist, ships adversarial model and safeguards
Enterprises are still scrambling to keep up with a threat that refuses to disappear.
Coforge launches EvolveOps.AI, AI‑agent platform to curb reactive IT ops
Coforge has rolled out EvolveOps.AI, a platform that positions autonomous AI agents at the heart of...
Manish Maheshwari's Fund Targets AI, Enterprise Software, Healthcare Seed Startups
Why should anyone caring about early‑stage tech watch Manish Maheshwari now? The answer lies in a...
Common Questions Answered
What are the three architectures that dominate the enterprise voice AI market?
The market is split into native S2S models like Google's Gemini Live and OpenAI's Realtime API, modular pipelines that provide granular control and audit trails, and a third hybrid approach that balances speed and cost. Each architecture prioritizes different trade‑offs between compliance, latency, and operational expense.
How do S2S models preserve paralinguistic signals compared to other architectures?
S2S (speech‑to‑speech) models process raw audio directly, keeping cues such as tone, hesitation, and emotion intact. This native handling enables richer user interactions but requires careful governance because the audio data remains in‑flight throughout processing.
Why are S2S models described as 'Half‑Cascades' rather than true end‑to‑end speech models?
Although S2S models ingest audio end‑to‑end, they still rely on intermediate stages that separate acoustic understanding from language generation, forming a half‑cascade architecture. This design allows preservation of paralinguistic information while still leveraging separate language components.
What compliance considerations influence the choice between a streaming S2S pipeline and a modular transcription‑first approach?
Regulators focus on how audio data is stored, processed, and audited; streaming S2S pipelines keep data in motion, reducing storage risk but limiting auditability. Modular pipelines, by transcribing first, create explicit logs and audit trails, offering stronger control for enterprises with strict compliance mandates.