The IT Nerd

Deepgram, the world’s most realistic and real-time Voice AI platform, today announced from VapiCon 2025 the launch of Flux, the world’s first conversational speech recognition (CSR) model designed specifically for real-time voice agents. Unlike traditional automatic speech recognition (ASR), which was built for passive transcription use cases like captions or meeting notes, Flux is trained to understand the nuances of dialogue. It doesn’t just capture what was said. It knows when a speaker has finished, when to respond, and how to keep the flow of conversation natural and engaging.

The global voice AI agents market is projected to reach nearly $47.5 billion by 2034, growing at a compound annual rate of about 34.8%. This growth is primarily due to the enterprise shift toward automated customer self-service, smarter agent assist tools, and embedded conversational experiences across industries. But traditional STT systems weren’t designed to participate in live dialogue. To recreate conversational flow, developers have been forced to piece together transcription, voice activity detection, and turn-taking logic — a patchwork that leads to latency, errors, and frustrating user experiences.

Flux eliminates these problems by embedding turn-taking directly into recognition. It transforms speech recognition from a passive recorder into an active conversational partner. This provides developers with the tools to build responsive, human-like voice agents without the complexity of workaround code or endless threshold tuning.

What Flux Delivers:

• Embedded turn-taking intelligence – Conversation-aware recognition that handles timing inside the model itself, with context-aware turn detection and native barge-in handling for fluid exchanges.
• Lightning-fast performance – Ultra-low latency where it matters most with ~260ms end-of-turn detection, plus distinct events to support eager response generation before a turn is complete.
• Simpler development – Turn-complete transcripts and structured conversational cues replace fragile client-side logic, so teams can ship production-ready agents in weeks, not months.
• Enterprise-ready scalability – Nova-3 level accuracy, GPU-efficient concurrency with 100+ streams per GPU, and predictable costs that avoid the hidden overhead of bolted-on systems.

Who It’s For: 

• Voice AI builders – Developers, engineering leads, and AI teams creating real-time agents.
• Enterprise innovators – Leaders modernizing customer experience with agent assist and conversational AI platforms.
• Ecosystem partners – Platform providers, consultancies, and cloud architects looking to integrate CSR into larger AI stacks.

Flux is generally available (GA) today. Developers can start building with CSR immediately.

To celebrate the launch, Deepgram is announcing OktoberTalk – making Flux FREE to use for the entire month of October. Developers can use Flux to build and test real-time voice agents at no cost, with support for up to 50 concurrent connections. The goal: remove every barrier to experimentation so teams can experience how conversational speech recognition changes what’s possible in voice AI. 

Deepgram today announced that Abby Connect, a premier virtual receptionist service, has successfully launched its new AI Receptionist product line built on Deepgram’s real-time speech-to-text technology. By choosing Deepgram, Abby Connect is scaling its high-touch customer experience while meeting the demanding needs of industries such as law, healthcare, and home services.

For more than 20 years, Abby Connect has built its reputation on creating a warm, human first impression for every call. But scaling that personal service 24/7 – while managing rising client demand and costs – presented a major challenge. Abby Connect turned to Deepgram to help strike the right balance between efficiency and empathy.

Why Abby Connect Chose Deepgram 

After evaluating Google Cloud Speech-to-Text, AWS Transcribe, AssemblyAI, and Whisper, Abby Connect found Deepgram’s performance to be unmatched:

• Accuracy in the Real World – Deepgram outperformed competitors on noisy calls, including from HVAC job sites.
• Low Latency for Natural Conversations – Sub-300ms streaming latency enabled real-time, two-way AI dialogue without delays.
• Ease of Integration – Developer-friendly APIs and transparent pricing simplified rollout.
• Domain Customization – Tuned for industry-specific terminology, from legal to medical.

Results Delivered

By leveraging both Deepgram’s real-time and pre-recorded transcription APIs, Abby Connect achieved measurable results:

• New AI Receptionist Product Line – Successfully launched, automating repetitive call types like scheduling and FAQs.
• 5x Boost in QA Productivity – Quality assurance teams now review five times more calls per day.
• 30% Reduction in Audit Time – Faster reviews mean stronger agent coaching and more consistent service.
• Scale to 100,000+ Calls per Month – Deepgram reliably transcribes massive call volumes to power both AI and human workflows.

Abby Connect is now exploring how to extend Deepgram-powered transcription into even more advanced conversational AI, including large language models trained on call data to detect intent, measure sentiment, and enable smarter escalations.

To learn more, please read the Abby Connect case study found here: https://deepgram.com/customers/abby-connect 

By Praveen Rangnath, CMO, Deepgram

If you’re in IT leadership, you’ve seen tech trends come and go. Some flare up in the headlines, get hyped at conferences, and then quietly fade away. Others — the rare ones — change the way entire industries operate.

Voice AI is one of the rare ones.

The problem is, a lot of people think they already understand it. They’ll nod along and say, “Oh yeah, like Siri or Alexa, right?” And if you leave it at that, you’ll miss the fact that Voice AI in the enterprise has almost nothing to do with consumer gadgets.

What we’re talking about is the ability to capture, understand, and act on every conversation your business has — with customers, employees, suppliers, regulators — in real time. It’s a shift from voice being something that “just happens” to voice becoming one of your most valuable data assets.

And here’s the kicker: if you’re the IT leader who drives that transformation, it won’t just make your company more competitive — it’ll make you more promotable.

Why This Is Bigger Than a Tech Upgrade

Think about all the conversations that happen in your organization in a single day:

• A frustrated customer explaining a problem to support.
• A nurse speaking with a patient about symptoms.
• A technician describing a fault to a field supervisor.
• A sales rep negotiating terms with a client.
• Two department heads debating how to allocate budget.

Historically, those conversations disappear the second they end. Maybe you get a line or two typed into a CRM. Maybe a call recording sits on a server somewhere, unlistened to. But you’re losing context, insights, and opportunities every single time.

Voice AI changes the game. It can:

• Transcribe those conversations in real time — accurately, even with accents, jargon, or background noise.
• Understand the meaning, not just the words.
• Detect urgency, emotion, and intent.
• Feed that intelligence into your existing systems so it’s searchable, reportable, and actionable.

This isn’t about making calls “sound nicer.” It’s about turning the most human part of business — conversation — into something you can measure, learn from, and improve at scale.

Why IT Management Should Own This

If you’re thinking, “This sounds like a customer service project,” you’re not wrong — but you’re not right, either.

Customer service, sales, compliance, operations — they all want the benefits. But none of them can roll this out enterprise-wide without IT leadership guiding the architecture, governance, and integrations.

That means you have a rare opportunity:

• Lead a high-impact initiative that crosses silos.
• Control the data governance and compliance from day one (critical if you’re in finance, healthcare, or government).
• Choose technology that scales, so this doesn’t become another point-solution mess.

When IT drives Voice AI adoption, it’s not just “supporting the business.” It’s reshaping how the business works. That’s the kind of strategic leadership that the C-suite and boards notice.

The Career Capital Play

Here’s the part some IT leaders miss: delivering a Voice AI initiative isn’t just good for the company — it’s good for your career.

1. You’ll be seen as an innovator — not just the person who keeps the lights on. You’ll have a real-world example of bringing in a new capability that directly ties to revenue, customer satisfaction, and operational efficiency.
   
2. You’ll get visibility with the C-suite — because every major function will be affected, and you’ll be the one making sure it all works.
   
3. You’ll have hard metrics to show — cost savings, reduced call times, faster onboarding, higher CSAT, better compliance records. These are the kind of results that get repeated in performance reviews.
   
4. You’ll build executive allies across departments — sales, marketing, operations, and compliance will all have wins they can point to because of your project. That makes you easier to promote.

If You Think You Already Understand It — This Could Be the Gap

A lot of tech leaders think Voice AI means “speech-to-text” plus maybe a chatbot. That’s like saying the internet is just “email plus websites.”

The real power is when Voice AI is:

• Real-time — not hours later.
• Context-aware — understanding your specific business language and workflows.
• Integrated — feeding into CRM, ERP, analytics, and compliance systems automatically.
• Scalable — able to handle every department, every language, every channel without breaking.

That’s when it stops being a tool and becomes infrastructure, and infrastructure projects with measurable business wins are the ones that get you invited to the big table.

Your Next Moves

If you want this to be a career-making initiative, here’s where you can start:

1. Map Your Conversation Ecosystem — Where in your organization do high-value voice interactions happen daily?
   
2. Identify High-Impact Use Cases — Pick two or three where you can prove ROI quickly (e.g., customer support, compliance-heavy calls, field service updates).
   
3. Get Cross-Functional Buy-In Early — Loop in operations, CX, compliance, and sales from day one.
   
4. Test for Accuracy First — Before you get dazzled by AI features, nail transcription quality. Everything else depends on it.
   
5. Plan for Scale — Choose solutions that can grow beyond your pilot without creating security or integration headaches.

Bottom line: Voice AI is more than a technology trend — it’s a platform for delivering visible, measurable business wins. If you own it, you don’t just modernize the company. You modernize your own career path.

The leaders who make this move now won’t just be part of the conversation. They’ll be running it.

Deepgram is joining with Amazon Web Services (AWS) to present a live webinar titled, “Building AI Voice Agents with Deepgram + AWS Bedrock” tomorrow, Tuesday, September 9, 10:30 AM – 11:30 AM PDT.

​Deepgram’s Voice Agent API brings lightning-fast speech-to-text and lifelike text-to-speech together with event hooks and speaker diarization, all in real time. Amazon Bedrock gives you instant access to leading foundation models like Claude and Titan, with built-in safety, compliance, and flexibility, perfect for powering voice agents with real intelligence. ​​

Attendees will learn how to build scalable, responsive AI voice agents that actually work in production.

What You’ll See & Learn:

• ​Build & Deploy in Minutes – See how Deepgram’s streaming API and Bedrock’s managed LLMs make real-time, voice-driven agents possible without stitching together brittle services.
• ​Smarts + Speed in Action – Watch a live demo that showcases accurate transcription, rapid LLM responses, and the power of few-shot or RAG-based responses, all with sub-second latency.
• ​Enterprise-Ready Architecture – Learn how to deploy with VPC, IAM, encryption, and autoscaling, all while controlling cost and optimizing performance.

Learn more and register here: https://luma.com/d3qf3t8s, or I would be happy to get you signed up to attend.

OpenAI just made an announcement titled, “Introducing gpt-realtime and Realtime API updates for production voice agents” found here: https://openai.com/index/introducing-gpt-realtime/

Scott Stephenson, CEO and Founder of Deepgram, would like to respectfully offer the following thoughts on this news:

“OpenAI’s new model shows progress, but the benchmarks make it clear: latency, turn-taking, and lack of control remain its Achilles’ heel in real conversations,” said Scott Stephenson, CEO and Founder, Deepgram. “When you measure what makes conversations actually work — speed, politeness, and turn-taking — Deepgram still leads the pack. The benchmarks confirm what users feel: conversations with Deepgram just flow more naturally.”

Stephenson continued, “Why does this matter? In real-world deployments, people don’t judge a voice agent by its feature set — they judge it by how the conversation feels. Latency and turn-taking aren’t technical footnotes; they’re the difference between a helpful interaction and a frustrating one. That’s why benchmarks that measure conversational flow, not just functionality, are the true indicator of readiness for production.”

Benchmarks That Back It Up 

• #1 across all tests: Deepgram ranked highest under every VAQI weighting — equal, politeness-heavy, and latency-heavy.
• Politer conversations: Fewest interruptions, meaning agents don’t talk over users. 
• Faster responses: Sub-second average latency (0.85s) vs. OpenAI’s 2.55s. 
• Smarter timing: Strong turn-taking with a competitive miss rate (0.427). 
•  Consistent edge: Even when benchmarks shifted priorities, results held — Deepgram stayed on top. 

Source: VAQI Benchmark, August 2025

Deepgram published a blog today with further details: https://deepgram.com/learn/vaqi-openai-gpt-realtime-test-with-sensitivity-analysis

Deepgram today announced that it has signed a strategic collaboration agreement (SCA) with Amazon Web Services (AWS). The multi-year agreement deepens Deepgram’s relationship with AWS and reflects a shared commitment to accelerating the development and adoption of generative voice AI. As part of the collaboration, Deepgram will expand co-selling and go-to-market efforts, integrate more deeply with AWS services, and empower enterprises to build scalable, high-accuracy voice applications across a wide range of use cases. 

Innovative startups and Fortune 100 enterprises alike are already transforming customer experiences using Deepgram and AWS. One Fortune 20 healthcare company uses Deepgram’s speech models on secure, scalable AWS infrastructure to modernize its contact center operations and deliver faster, more personalized customer support.

As a Generative AI Competency Partner and long-standing AWS Partner Network (APN) member, Deepgram offers a full-featured voice AI platform that includes speech-to-text (STT), text-to-speech (TTS), and speech-to-speech (STS) capabilities. Additionally, Deepgram’s Dedicated deployment and EU endpoints run entirely on AWS infrastructure, enabling enterprise customers to meet global requirements for data residency, security, and compliance.

Deepgram’s infrastructure is deeply integrated with AWS, enabling customers to deploy its platform on Amazon EKS for scalable container orchestration, store data securely with Amazon S3, and manage containers using Amazon ECR. Customers can also use Amazon API Gateway and AWS Lambda to securely orchestrate interactions between Deepgram’s voice AI APIs and other services, including Amazon Bedrock hosted models and enterprise systems. Whether deployed in a customer-owned VPC or as a fully managed SaaS environment, Deepgram offers the flexibility required to maintain compliance, ensure data control, and operate efficiently at scale. Looking ahead, Deepgram plans to expand availability through AWS services like Amazon SageMaker and Amazon Bedrock to further streamline AI model deployment and orchestration.

Deepgram’s speech-to-text API can also be integrated into Amazon Connect, enabling best-in-class STT speed and accuracy for real-time transcription and voice automation within contact center environments. This helps enterprises improve agent productivity, automate call summaries, and enhance customer experiences.

As part of the SCA, Deepgram will invest in building GenAI-enabled capabilities on AWS, deliver new case studies and proof-of-concepts for enterprise customers, and continue optimizing its models and services for the AWS ecosystem.

Deepgram’s availability in AWS Marketplace also simplifies procurement for engineering and infrastructure teams by enabling usage-based pricing, unified billing, and rapid deployment within existing AWS environments.

Learn more about the partnership by visiting deepgram.com/aws, or start building with Deepgram on AWS today by exploring their listing on the AWS Marketplace.

Voice AI is rapidly becoming foundational infrastructure across industries, powering real-time agents, compliance-sensitive workflows, and multilingual applications at scale. As global adoption accelerates, so does the demand for flexible deployment models, regional hosting, and production-grade reliability.

To meet that demand, Deepgram is announcing two major infrastructure expansions:

• The general availability of Deepgram Dedicated, a fully managed, single-tenant runtime
• The early access launch of our EU-hosted API endpoint, enabling in-region inference for European workloads

These launches reflect a broader shift in how voice AI is being deployed, and they come at a time of growing industry validation. This month, Deepgram Nova-3 was named a 2025 Voice AI Technology Excellence Award winner by TMC’s CUSTOMER magazine, recognizing their leadership in accuracy, real-time multilingual transcription, and self-serve customization.

Together, these milestones reinforce Deepgram’s commitment to providing voice AI infrastructure that supports enterprise-scale performance, compliance, and geographic flexibility.

What It Means to Go Global with Voice AI

Going global starts with supporting the world’s languages. Deepgram already supports over 36 languages for customers worldwide and will continue expanding language coverage throughout 2025. 

But language support is only the beginning.

For engineering teams building production-grade systems, global voice AI also requires solving for infrastructure and compliance demands as workloads expand across regions. As enterprises scale voice workloads globally, we continue to hear two common friction points: the growing complexity of managing infrastructure across regions and tightening data policies, particularly in the EU, that require stricter control over where and how voice data is processed.

These demands include:

• Ultra low-latency inference paths. Real-time applications require models to run as close to the end user as possible to minimize round-trip time and meet interaction thresholds.
• Data residency and legal jurisdiction. Voice data often must be processed and stored within specific geographic boundaries to meet regulatory requirements such as GDPR.
• Single-tenant isolation for sensitive workloads. Some environments require dedicated infrastructure to enforce data segregation, meet compliance standards, or satisfy internal security policies.
• Scalable operations without added DevOps burden. Expanding voice workloads across regions should not require a proportional increase in infrastructure engineering.

Deepgram’s platform was designed with these requirements in mind, providing the foundation needed to operationalize voice AI reliably and securely across global environments.

Introducing Deepgram Dedicated: A Managed, Single-Tenant Runtime

Enterprises adopting voice AI at scale often face a difficult tradeoff: maintain control over infrastructure and data by self-hosting, or prioritize ease of use through shared, multi-tenant cloud APIs. Self-hosting offers isolation and regional control, but introduces significant ongoing operational complexity. Managed service providers can help bridge the gap, but they often lack product-level expertise and introduce dependency overhead that slows down feature adoption.

Now generally available, Deepgram Dedicated closes this gap. It is a fully managed, single-tenant deployment of Deepgram’s voice AI platform that offers the control and flexibility of self-hosted infrastructure without the burden of operating it. Over the past six months, it has been selectively deployed with a select group of enterprise customers in early production deployments across a range of use cases, from real-time contact center platforms to globally distributed voice agents.

Teams gain regional isolation, performance control, and compliance alignment while offloading infrastructure management to Deepgram. Deepgram Dedicated currently runs on AWS, with support for additional cloud providers on the roadmap.

Key Highlights:

• Single-tenant architecture: Each deployment runs on isolated compute, avoiding noisy neighbor effects and supporting strict data segregation.
• Unified voice AI stack: Run speech-to-text, text-to-speech, and speech-to-speech workloads in a single runtime with consistent API behavior.
• Multi-cluster design: Separates real-time, pre-recorded, and agent workloads onto specialized clusters to maximize performance, ensure high availability, and enable strict workload isolation.
• Region-specific infrastructure: Deploy in your preferred cloud region to meet compliance requirements, enable ultra-low latency, and align with internal policies, including support for country-level deployments.
• SLA-backed performance: Optional SLAs ensure predictable uptime and latency with defined targets monitored and enforced by Deepgram.

In one modeled scenario, a customer supporting 1,000 concurrent real-time streams would spend approximately $467K USD annually if self-hosting. This includes $250K in DevOps headcount and $98K in infrastructure costs.

Running the same workload on Deepgram Dedicated lowers total OPEX by approximately $98K USD per year. It also reduces engineering overhead and improves deployment reliability through platform-managed SLAs and regional isolation, giving teams more time to focus on higher-impact work.

EU-Hosted API Endpoint: In-Region Inference for European Voice Workloads

Voice AI adoption is accelerating across Europe, driven by demand for real-time applications in finance, public services, retail, and telecommunications. To date, more than two dozen customers and prospects have expressed interest in EU-based infrastructure, highlighting growing demand for in-region processing that meets local performance expectations and regulatory requirements without compromising model quality or flexibility.

To support this, Deepgram is launching early access to api.eu.deepgram.com, a new EU-hosted speech-to-text API endpoint that delivers in-region inference with full feature parity and consistent performance. The EU endpoint is hosted in AWS EU regions, with additional hosting options under consideration.

Key Highlights:

• Voice data stays within the EU. All processing occurs inside EU-based AWS regions, ensuring no cross-border data transfer.
• Latency improvements for EU-based users: Localized inference reduces round-trip time for applications serving users in or near the EU.
• No code changes required: Existing integrations can migrate by updating the base URL, with no other changes needed.
• Supports GDPR compliance and auditability: The deployment is fully isolated within the EU legal boundary and aligned with regional data protection standards.

This endpoint is well-suited for European ISVs, compliance-focused enterprises, and global teams looking to reduce latency and streamline deployment in the EU.

Why This Matters: A Global-Ready Voice AI Platform

With these additions, Deepgram now supports a range of deployment options, including multi-tenant hosted APIs, fully managed single-tenant deployments, and customer-operated self-hosted infrastructure. This flexibility allows engineering teams to choose the right model based on their application requirements, compliance obligations, and operational preferences. For some, the hosted API provides a fast path to integration. Others may require the regional data residency of the EU endpoint or the isolation and control of a Dedicated deployment. Teams with existing DevOps capacity may opt for self-hosting to align with internal security policies or infrastructure standards.

What differentiates Deepgram is the ability to deliver true flexibility across deployment models. Teams can build and scale voice AI systems using consistent APIs and model performance, while choosing the infrastructure that fits their environment. Looking ahead, the roadmap includes customer VPC deployments, BYOC support, and expanded region availability across Asia-Pacific, EMEA, and LATAM.

Start Building for Your Environment

If you’re building voice applications that require global reach, regulatory alignment, or low-latency performance, now is the time to explore your deployment options. Demand is high, and we’re expanding access selectively:

• Learn more about Deepgram Dedicated and request access to join the next wave of deployments
• Request early access to the EU endpoint

Deepgram now runs where your business runs. No trade-offs. No overhead. Just voice AI on your terms.

Deepgram, the leading voice AI platform for enterprise use cases, today announced the launch of Deepgram Saga, a Voice Operating System (OS) designed specifically for developers. Saga is a universal voice interface that embeds directly into developer workflows, allowing users to control their tech stack through natural speech. Unlike traditional voice assistants that pull developers out of their flow, Saga sits on top of existing tools, transforming rough ideas into precise AI coding prompts, executing multi-step workflows across platforms via Model Context Protocol (MCP), and eliminating the constant context switching that fragments modern development.

In today’s development environment, engineers routinely juggle 8+ tools across multiple monitors, constantly translating thoughts into clicks, rough ideas into overly specific prompts, and context into commands. This fragmentation creates a “quiet tax” on productivity — time lost to alt-tabbing, window hunting, and manual navigation between coding, testing, and deployment tools. Saga eliminates this friction by providing a voice-native AI interface that interprets developer intent and executes actions across the entire tech stack, enabling developers to stay in flow while building software.

Voice-First Workflow Control

Saga addresses the core challenges facing AI-native developers and early-stage builders who need to move fast without getting bogged down in tool complexity. 

Key capabilities include:

• Developer Ecosystem Friendly: Whether vibe coding with Cursor or Windsurf, maintaining status updates in Linear, Asana, Jira or Slack, extracting CSS from Figma designs, or just executing operational day-to-day tasks within Google Docs, Gmail or Google Sheets, Saga lives alongside the tools developers already know, love, and use every day.

• Intelligent Prompt Generation: Developers can speak vague ideas like “Build a Slack bot that reacts to emoji,” and Saga transforms these into crystal-clear, one-shot prompts for tools like Cursor, eliminating the trial-and-error cycle of “vibe coding.”

• End-to-End Workflow Execution: A single voice command like “Run tests, commit changes, deploy, and update the team” triggers coordinated actions across the entire development stack — no tabs, manual commands, or context switching required.

• Real-Time Documentation: Saga captures stream-of-consciousness thinking and transforms it into structured documentation, tickets, or PR descriptions, allowing developers to rubber-duck their way to clean documentation without breaking their train of thought.

• Contextual Tool Integration: Rather than requiring developers to switch to separate AI chat windows, Saga surfaces answers and executes actions inline, layered over existing development tools.

• Natural Code Generation: Developers can speak requests like “Get me the top 10 users who signed up in the last week” and receive instant SQL or JavaScript snippets without needing to Google syntax or write boilerplate.

Built for AI-Native Development with MCP

Saga is specifically designed for the new generation of technical users who rely on AI agents, use tools like Cursor and Windsurf daily, and treat their workflow like a programmable operating system. The platform integrates seamlessly with existing developer tools through MCP (Model Context Protocol) and other standard interfaces, ensuring teams can adopt Saga without disrupting their current setup.

Enterprise-Grade Voice Intelligence

Built on Deepgram’s world-class speech-to-text, text-to-speech, and voice agent APIs, Saga delivers the accuracy and responsiveness required for mission-critical development workflows. The platform understands technical context, domain-specific terminology, and the nuanced language developers use when thinking through complex problems.

Unlike consumer voice assistants that require rigid command structures, Saga interprets natural, conversational speech and translates it into precise technical actions. This approach eliminates the cognitive overhead of remembering specific voice commands while maintaining the reliability enterprises need for production development environments.

Start Building with Saga

Experience how voice can transform your development workflow with Deepgram Saga. The platform is designed for developers who want fewer clicks and more execution, enabling faster iteration cycles and reduced context switching. 

Additional Resources

–      Get started with Deepgram’s quickstart guides

Deepgram has officially expanded its Aura-2 text-to-speech (TTS) API with a new suite of high-quality Spanish voice models, bringing realistic, expressive, and business-ready voice synthesis to Spanish-speaking markets.

This launch marks a major step in Deepgram’s mission to enable real-time, natural-sounding voice experiences across global industries. The new Spanish voices are optimized for enterprise use cases, from customer support and IVR systems to healthcare and education, featuring precise pronunciation for currencies, timestamps, acronyms, emails, and more.

HIGHLIGHTS:

• 10 new Spanish Aura-2 voice models tailored for professional use
• Support for Mexican, Peninsular, Colombian, and Latin American accents
• Models designed for diverse applications including advertising, IVR, storytelling, and customer service
• Support for code-switching in select models (English ↔ Spanish)
• Available now via REST and Websocket APIs

Voices like “Celeste” (Colombian, energetic and friendly) and “Nestor” (Peninsular, calm and confident) are just a couple of the expressive voices now available.

It is available now for use via Deepgram’s hosted TTS API platform. 

Here is a blog with details: https://deepgram.com/changelog/aura-2-spanish-tts 

Developers and product teams can find implementation examples and model specifications in the Deepgram Developer Documentation, here: https://developers.deepgram.com/docs/tts-models?_gl=1*1azt33a*_gcl_au*NzM3MTk0MjU1LjE3NDQ2NjQxNTE.*_ga*OTEzNjY5NzcyLjE3MzY4MDM3ODc.*_ga_TYPC1TBCKT*czE3NTA5NjIyNTckbzkyJGcxJHQxNzUwOTY0ODE3JGozMyRsMCRoMA..#aura-2-all-available-voices 

Deepgramtoday announced the launch of The Scott Stephenson AI Show, a new podcast hosted by Scott Stephenson, CEO and Co-Founder of Deepgram. In each episode, Stephenson explores the fast-changing world of artificial intelligence (AI), cutting through the hype and digging into what’s actually happening under the hood of today’s most powerful AI technologies. Stephenson brings his signature candor, industry insight, and curiosity to every topic, offering unfiltered perspectives on what’s working, what’s hype, and what’s next.

Episode 1

In the first episode, Scott unpacks the concept of vibe coding, the rising trend where developers interact with AI in a product manager-like mindset, using natural language and feedback instead of conventional code. He also explores the emerging era of AI agents, A2A (agent-to-agent) communication, MCP (Model Context Protocol), and how these breakthroughs will reshape engineering and business workflows.

Episodes will be released bi-weekly. Future episodes will feature conversations around evaluating GenAI models, what and who to trust, and constraints and accelerators for the pace of innovation.  

Where to Watch and Subscribe:

• Spotify: https://open.spotify.com/show/1aM133CZK6aMeQASltnlrB
• YouTube: https://www.youtube.com/playlist?list=PLodqEmEyiQa0lC6cMEoUpLrW7GH2aGd9M