SigmaMind AI Powers a Million Monthly Voice Agent Calls with Deepgram’s Real-Time Speech-to-Text

SigmaMind AI builds the orchestration layer between raw audio and intelligent action. The company’s no-code platform lets developers and enterprises deploy production-grade voice AI agents for sales, support, and operations: agents that don’t just listen, but reason, call APIs, and complete tasks in real time. With customers routing over a million calls per month through the platform, the accuracy and speed of the speech-to-text layer isn’t a nice-to-have. It’s the foundation everything else depends on.

By integrating Deepgram’s Nova-3, and Flux speech-to-text models as the default real-time transcription engine, SigmaMind reduced end-to-end agent response latency by roughly 300 milliseconds and enabled a new class of voice workflows where agents act on speech before a sentence is even finished.

• 1 million+ calls per month processed through SigmaMind’s platform, with 200+ hours of speech transcribed daily
• ~300ms reduction in end-to-end agent response time after integrating Deepgram’s streaming STT
• Sub-1-second voice-to-voice latency including telephony overhead, enabling natural conversational pacing
• 150 peak concurrent voice sessions handled per customer deployment without degradation
• 50% increase in outbound call conversion for a call center customer that migrated to SigmaMind, going live in just two weeks

For startups, agencies, and call centers looking to deploy voice AI, the gap between “working demo” and “production system” is enormous. Building a voice agent that handles real conversations, with interruptions, mid-sentence corrections, background noise, and sub-second response expectations, requires stitching together STT, TTS, LLMs, telephony, and tool integrations into a pipeline that holds up under load.

SigmaMind set out to collapse that gap by providing the orchestration layer: models, telephony, API connections, testing tools, and deployment infrastructure packaged so builders can focus on agent behavior, not audio plumbing.

But the orchestration layer is only as good as the components it orchestrates. The STT provider sits at the very front of the pipeline, and its performance cascades:

• Every millisecond of transcription latency compounds downstream through LLM reasoning, tool calls, and TTS generation
• Every transcription error produces the wrong LLM response, the wrong API call, and the wrong customer experience
• Inconsistent performance under concurrent load makes scaling unpredictable

The team needed an STT layer that met specific production requirements:

• High accuracy under real telephony conditions, not just clean audio benchmarks
• Consistent performance at 100+ concurrent sessions
• Real-time streaming with usable interim transcripts — not just final results
• Multilingual support for global deployments
• Cost predictability at millions of minutes per month

SigmaMind evaluated several STT providers before choosing a primary partner. The evaluation centered on capabilities that directly affect voice agent performance in production:

• Interim transcripts for mid-utterance action: Deepgram’s streaming API returns interim results fast enough for SigmaMind’s orchestration engine to start reasoning before a user finishes speaking, eliminating the perceptible lag that breaks conversational rhythm
• Flexible audio format support: Native Opus and PCM handling reduced CPU and transcoding overhead in SigmaMind’s LiveKit-based audio pipeline
• Word-level timestamps and punctuation: Precise alignment between transcript segments and audio tracks enabled speaker attribution, time-accurate tool calls, and downstream analytics
• Custom vocabulary and speech context: Improved recognition of product names, acronyms, and domain-specific terms without heavy post-processing
• Enterprise security: TLS encryption, token-based authentication, configurable data retention, and PII redaction met the compliance requirements of SigmaMind’s enterprise customers
• SDK ergonomics: Deepgram’s SDKs and real-time APIs reduced engineering effort from prototype to production

“When we began acting on interim transcripts and combined that with word timestamps, the agent could trigger API calls and follow-ups mid-utterance,” said Pratik Mundra, co-founder of SigmaMind AI. “That shift unlocked much richer, multi-step voice workflows.”

A typical voice interaction on SigmaMind follows this flow:

1. A user connects through a phone call, web interface, or embedded application
2. Live audio is managed through LiveKit for real-time media transport and simultaneously routed to Deepgram for transcription
3. Deepgram processes incoming audio and returns interim and final transcripts with punctuation, timestamps, and confidence scores
4. Transcripts feed into SigmaMind’s orchestration engine, which interprets intent using LLMs (the platform supports models from OpenAI, Google, and Anthropic)
5. The agent determines the next action, a follow-up question, an API call, a CRM update, or a state change
6. Text-to-speech converts the response to audio and streams it back through the same LiveKit session

Deepgram continues transcribing each utterance throughout the interaction, maintaining conversational context. Final transcripts are stored within SigmaMind for analytics, conversation insights, and debugging.

The platform currently uses Deepgram’s Nova-3, and Flux models. Model selection is abstracted from end users and optimized internally based on streaming latency, endpointing reliability, telephony audio performance, and accuracy for tool-triggering phrases. Live features include streaming STT, smart formatting and punctuation, and keyterm prompting.

Since integrating Deepgram, SigmaMind has observed measurable improvements across several dimensions of their voice agent platform.

Latency and responsiveness:

• End-to-end latency decreased by approximately 300 milliseconds
• Agents begin processing before utterances finish, making conversations feel significantly more responsive
• Sub-one-second voice-to-voice latency maintained even with ~200ms of telephony overhead

Transcription quality:

• Fewer misunderstandings on domain-specific terms, thanks to custom vocabulary support
• Cleaner speaker attribution and richer transcript metadata via word-level timestamps mapped to LiveKit track IDs
• Reduced manual corrections and downstream retries in agent workflows

Customer impact:

• One call center that migrated to SigmaMind from a competing platform saw its outbound call conversion rate jump by 50%
• That customer went live in just two weeks and now routes over 25,000 calls per day, with peak concurrency reaching 160 simultaneous voice sessions
• During a live enterprise demo, a user repeatedly interrupted the agent mid-conversation, the agent handled each interruption correctly and completed the workflow without restarting

“Voice AI is a systems problem, not just a model problem,” said Mundra. “Improvements in one model don’t translate to better outcomes unless the entire pipeline works reliably and with low latency.”

SigmaMind’s roadmap is focused on pushing voice agents closer to production-grade reliability, deeper system integrations, and more natural conversations.

Near-term priorities include:

• Latency and turn-taking: Continued optimization of the voice pipeline to improve interruption handling, barge-in detection, and conversational pacing
• Batch transcription: Post-call analytics, QA workflows, and structured reporting to complement real-time streaming
• Enterprise integrations: Deeper CRM, support tool, and scheduling system connectivity as the customer base grows into regulated industries like healthcare, financial services, and insurance
• Developer tooling: MCP server support and modular agent components so teams can integrate the platform into existing developer workflows

At a million calls per month and growing — with an expectation of 10x growth in the next six months — the demands on the real-time transcription layer will only increase. The partnership between SigmaMind and Deepgram is built around a shared assumption: that voice AI at production scale requires not just accurate models, but reliable, observable, and composable infrastructure that holds up when the volume spikes and conversations get messy.