Murf.AI is one of the world's fastest-growing AI voice companies, building next-generation text-to-speech and voice generation technologies that enable enterprises and developers to create natural, high-quality voice experiences at scale.
The company claims to serve more than 8 million users and work with over 300 Forbes 2000 companies, powering voice applications across customer engagement, marketing, training, product experiences, and entertainment.
At the core of its offerings is the Murf Falcon Text-to-Speech API, which enables developers to seamlessly integrate lifelike AI voices into applications, voice agents, and automated workflows.
In an exclusive interview with THE WEEK, Murf.AI COO Sneha Roy speaks on a wide range of issues, from innovations in voice AI and the ethical challenges surrounding the technology to the compute infrastructure and policy decisions that governments need to address.
Q/ How do you define "human-like, high-quality voice experiences at scale," and what metrics do you use internally to measure success against this vision? Are there trade-offs between quality, speed and cost that you've had to navigate?
A/ I think we've been asking the wrong question when it comes to voice AI. For a long time, everyone focused on, "Does it sound realistic?" But that's only part of what makes a conversation feel human. The other, equally important factor is timing. Think about it: in a real conversation, we respond to each other in about 200 milliseconds. That's the rhythm our brains are wired for.
So even if someone can't quite put their finger on why a conversation with an AI feels off, more often than not, it's because that rhythm got disrupted somewhere. Today, with Falcon 2 delivering highly natural-sounding speech, we believe both factors matter equally: voice quality and conversational timing. It's the combination of realistic speech and human-like responsiveness that makes an AI conversation truly feel natural.
That's why, at Murf, we don't just obsess over how a voice sounds—things like pronunciation, naturalness, or how well it handles different languages. We're just as focused on how fast that first bit of audio comes back, how consistent the latency is, and whether that holds up no matter where in the world someone's using it. Because a voice can sound beautiful and still ruin the experience if it's slow or unpredictable.
And here's the thing: I don't think you actually have to choose between quality, speed, and cost. That trade-off isn't as fixed as people assume. A lot of it comes down to smart architecture decisions, building lighter, more efficient models, and deploying closer to the edge. Do that well, and you can get great voice quality, low latency, and cost efficiency all at once. So for us, it's never about chasing one metric. It's about engineering the whole stack so that conversations just feel natural.
Q/ Can you walk us through the architecture of the Murf Falcon Text-to-Speech API? What proprietary techniques or model innovations differentiate it from competitors in terms of naturalness, prosody, emotional range and customisation?
A/ Murf Falcon was actually built as our own text-to-speech foundation model from scratch. We didn't build it on top of an existing LLM. That was a deliberate choice; it gives us full control over the architecture and the whole inference pipeline, instead of inheriting someone else's tradeoffs. At the core of it is a lightweight, compute-efficient neural architecture that we designed specifically for real-time speech generation.
The result is the fastest text-to-speech model available today, delivering natural-sounding voice quality without carrying all the computational baggage that comes with bigger models.
One great thing about Falcon is how it handles phonemes and voice identity separately. That sounds technical, but here's what it means in practice: pronunciation, prosody, accents, all the language-specific stuff, can be handled independently of the voice itself.
So when someone code-switches mid-sentence, say they drop into another language halfway through, the accent doesn't awkwardly bleed over. It just sounds natural, the way a real bilingual speaker would say it.
It's not only about the model either. We deploy at the edge across multiple geographies, which is a big part of why performance stays consistent no matter where someone's using it. Ultimately, what we're chasing isn't 'does this sound realistic.' It's 'does this sound like it understands the context, does it feel expressive, does it feel like a real conversation.'
We also give developers a lot of control over pacing, pauses, pronunciation, and tone, so they can shape it for their use case. And consistency matters just as much as expressiveness to us. A voice that sounds natural in one line but loses its personality by the next isn't really doing its job.
Q/ How does Murf.AI allocate compute resources—GPUs, TPUs and cloud infrastructure—across model training, inference, R&D experimentation and production workloads? How might broader AI trends, such as agentic systems and improvements in hardware efficiency, affect Murf.AI's competitive moat and business model?
A/ When it comes to compute, we're not chasing scale for the sake of scale. It's really about efficiency. We think about it as two different workloads. There's training, which happens in scheduled bursts on reserved GPU capacity, and then there's real-time inference, which is serving live voice traffic around the clock. That second one is really what eats up most of our day-to-day compute, since it never really stops.
So rather than just piling on more GPUs, our whole approach is about matching capacity to actual demand. We're dynamically picking the most cost-efficient GPU infrastructure across regions and making sure we're not sitting on idle hardware we don't need. And honestly, this only works because of how Falcon is built. Because the architecture is so lightweight and compute efficient, we get the capabilities you'd expect from a much bigger model, but without needing the infrastructure a bigger model would demand. That's where both the performance and the cost efficiency come from.
Looking ahead, I actually think trends like agentic AI validate this approach rather than force us to rethink it. As voice becomes the natural way people interact with AI agents, the need for fast, reliable, low-latency speech infrastructure is only going to grow. And at the same time, hardware itself keeps getting more efficient, so we can pull more performance out of the same infrastructure instead of just throwing more GPUs at the problem. I think our edge comes from pairing purpose-built speech models with smart, efficient deployment. That combination is what lets us keep latency low, performance consistent, and costs down, even as adoption scales up.
Q/ How do you approach ethical AI in voice technology, particularly in preventing deepfakes and misinformation, ensuring consent for voice cloning, and mitigating bias in synthesised speech? What internal guardrails or auditing processes do you have in place, and how have they evolved amid growing regulatory scrutiny?
A/ For us, ethical AI really starts with one belief: consent isn't something you bolt on later; it has to be part of the design from day one. So every single voice we train our models on is ethically sourced. We partner with professional voice actors who explicitly consent to how their voice is used; they're contractually licensed, and they get ongoing royalties every time their voice is actually used. That's non-negotiable for us.
We also made a deliberate choice not to offer self-service voice cloning. The risks there are just too significant. Enterprise voice cloning is available, but only in a controlled environment; customers go through verification, there are contractual safeguards, and before we ever create a clone, we validate voice ownership through consent prompts and voice similarity checks.
Beyond consent, we've built in safeguards to catch and prevent prohibited content, so the platform isn't misused for anything illicit or harmful. We also invest heavily in mitigating bias, training on diverse datasets across languages, accents, ethnicities and geographies, with a lot of focus specifically on multilingual and code-mixed speech, since that's genuinely hard to get right. And as regulations keep evolving, our governance framework stays focused on transparency, traceability, data privacy and enterprise-grade compliance. We back that up with standards like SOC 2 Type II, ISO 27001, GDPR and HIPAA.
Q/ What have been the biggest operational bottlenecks as Murf.AI scaled to more than eight million users? How do varying international regulations—such as the EU AI Act, data localisation requirements and evolving copyright rules for synthetic voices—affect your operations?
A/ We've scaled to over 10 million users globally, and the hardest part is delivering voice AI that actually holds up, consistently, in production, across languages, across geographies, across all these different enterprise environments. People underestimate how much more complex real-time voice gets once you factor in multilingual conversations, code mixing, noisy environments, getting text-to-speech right, keeping the conversation flowing naturally, all while staying low latency at scale. Those aren't problems you solve by just making the model bigger. They're engineering problems; they need sophisticated infrastructure and solid fallback mechanisms.
On the regulatory side, we've always seen compliance less as a constraint and more as an actual product capability. Enterprise customers, especially in regulated sectors, increasingly expect things like regional data residency, on-premises deployment options, and real governance around how their data is handled. So we've built our platform to support exactly that, with regional deployment controls and compliance across standards like GDPR, SOC 2 Type II, ISO 27001 and HIPAA.
When it comes to the copyright conversations happening around synthetic voices right now, that just reinforces the approach we've had since day one. Every voice in our models is explicitly licensed, contractually governed, and royalty-backed. As AI regulation matures globally, transparent governance and enterprise-grade compliance are only going to become bigger differentiators.
Q/ What specific policy recommendations would you offer governments on issues such as access to AI compute for startups, data-sharing frameworks and standards for labelling AI-generated content?
A/ There are three areas that really deserve attention here. First, access to AI compute needs to be democratised. Right now, startups building foundational AI can get boxed out simply because of infrastructure costs, and that's not really a fair fight. Something like shared national compute infrastructure, or targeted compute credits, could let early-stage companies compete.
Second, we need better data sharing frameworks that actually encourage high-quality, ethically sourced datasets, built on clear consent, licensing and governance. The thing people often get wrong about voice AI specifically is that the challenge usually isn't the amount of data. It's access to data that's genuinely diverse, multilingual, code-mixed, data that actually reflects how people talk in real life.
Finally, when it comes to standards for AI-generated content, I think the focus should be on transparency, not on restricting innovation. Clear disclosure, provenance mechanisms, watermarking—these build trust without slowing anyone down. And consent-based licensing really should become the norm for voice cloning and synthetic media at this point. Regulation works best when it's risk-based, when it protects users and creators, but doesn't pile on so much compliance burden that it stops startups from building AI technologies that can actually compete globally.
Q/ Looking three to five years ahead, what does success for Murf.AI look like in terms of market leadership, technological breakthroughs and societal impact? How do you expect advances in multimodal AI to shape the company's product roadmap?
A/ Over the next three to five years, success for Murf will be defined by becoming the foundational voice infrastructure powering the next generation of AI applications.
We see voice moving from being just a content creation tool to becoming the primary way people interact with AI agents. So our goal is to build the infrastructure underneath that, the thing that makes those conversations feel fast, natural and trustworthy, no matter what language or where in the world you are.
On the technology side, we're focused on pushing latency down even further, expanding multilingual capabilities, getting code-mixed speech right, and building voice models that are more context-aware and expressive, all while keeping the reliability and governance enterprise customers need. We also think multimodal AI is going to make voice interactions so much richer. Systems that can move seamlessly between text, speech, images and video. So we don't really see voice as this standalone thing anymore. It's becoming a core layer within these bigger multimodal experiences.
What we're really after is helping businesses deploy AI that feels as natural as talking to another person, while making sure those interactions stay transparent, consent-first, and something that can scale globally.
