Deepgram highlights faster, lower-cost approaches to voice AI for enterprises
Voice AI company Deepgram has outlined how different technical approaches to voice intent detection affect speed, cost and compliance, arguing that simpler, task-focused models can outperform larger systems in real-world use.
Deepgram said businesses building voice-powered systems face key trade-offs between accuracy, speed and cost when deciding how to process spoken commands and intent.
The company compared traditional two-step systems, which first convert speech to text and then analyse meaning, with newer end-to-end approaches that handle both tasks at once. It said two-step pipelines typically add around one to 1.2 seconds of delay, while end-to-end systems can respond in a few hundred milliseconds, making interactions feel far more immediate.
Deepgram said combining speech recognition and intent detection into a single, unified model can cut response times by more than two-thirds compared with older approaches.
The company also said smaller, task-specific AI models often make more sense for enterprise use than large general-purpose language models. While large models can be slightly more accurate, Deepgram said models such as BERT deliver much higher throughput and major cost savings, making them better suited to high-volume, real-time applications.
Deepgram added that compliance requirements strongly influence how voice AI systems are deployed. It said healthcare rules such as HIPAA allow cloud-based systems if strict safeguards are in place, while payment security standards such as PCI-DSS restrict how card data can be handled.
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The company pointed to industry examples showing that hybrid systems combining on-device and cloud processing can meet multiple regulatory standards in production.
Deepgram said its Speech-to-Text and Voice Agent APIs are designed to support production voice intent detection, with sub-300 millisecond transcription speeds and high accuracy even in noisy environments. The company added that it offers free credits for developers to test its technology through the Deepgram Console.
The Recap
- Deepgram compared two-step and end-to-end intent detection architectures.
- End-to-end models reduce latency by 68 percent versus cascade systems.
- Self-hosting becomes economical above ten to twenty million requests monthly.
