PersonaPlex is an impressive advance in full-duplex conversational AI, especially in how it combines natural turn-taking with flexible persona and voice control. Compared to systems like Coldi AI, which emphasize persona consistency and role adaptation primarily at the language level, PersonaPlex stands out by tightly integrating voice, prosody, and real-time interaction. Together, they highlight two complementary paths toward more human-like conversational agents.
It might ne an issue to choose which automation tools to use considering the specifics of business, from my experience, for call automation using voice AI works well, been checking https://coldi.ai/ for that purpose, works well for customer outreach.
I would agree that AI not interrupting doesnt sound like a natural human like conversation, would be interesting to see how are solving this problem. We've been checking https://coldi.ai/ cause they offer naturally speaking calling agents but they do not interrupt, and there is also a question, how AI deals with off topic questions from the customers.
Strong take, this clearly resonates with what we see in real deployments: the hard problems in voice AI are orchestration, latency, state, and safe action-taking, not model quality. A good choice would be to choose the service providing ready AI agents like https://coldi.ai/: it is built around end-to-end call reliability (fast turn-taking, barge-in, system integrations, and clean escalation), so teams can trust the outcome of real customer calls, not just demos.
This resonates strongly, especially the shift in the build vs buy calculus happening almost invisibly. What feels under-discussed is that agents are not just reducing the cost of building software — they are collapsing the coordination and abstraction costs that SaaS historically justified.
One emerging pattern I’m seeing is teams moving toward agent-orchestrated internal tooling rather than traditional “one-off” internal apps. Instead of rebuilding entire SaaS products, they compose narrow, task-specific agents that sit directly on top of their own data and workflows. This avoids both feature bloat and long-term roadmap dependency while keeping maintenance tractable. Platforms like Coldi AI, for instance, point toward this middle ground: not “replace all SaaS,” but replace the parts that are really just opinionated CRUD, dashboards, or glue code with agents that can evolve alongside the organization. That feels like the real threat surface for SaaS — not total displacement, but systematic erosion of NRR as customers internalize more of the value chain.
I too was excited when transformer-based models burst onto the scene — chatbots suddenly seemed smarter and more flexible than the old rule-based AI systems. Those early models were rough around the edges and sometimes produced garbage, but when they hit, it really felt like a step forward. The problem now is that these models always pick a next token — even when they have no good basis for it — so they can generate plausible but wrong or misleading content, and you often can’t tell the difference. That fundamental limitation means they aren’t reliable enough for high-stakes tasks like medicine, law, or financial decisions. Meanwhile, voice-based AI has also been growing rapidly this year, Coldi AI is a good example, with many more people and companies adopting assistants and voice interfaces.
I love the idea of this kind of automation when AI takes repetitive and time consuming tasks and you can concentrate on business growth. I am watching how Voice AI is growing this year, like crazy. Automation with AI voice agents is transforming how businesses manage inbound and outbound calls. Advanced systems can now handle lead qualification, appointment scheduling, and customer support with human-like empathy and natural speech. Platforms like Coldi AI are leading the way, creating voice agents so natural that customers often don’t realize they’re speaking to AI. Other major players, including Poly AI, Replicant, and Observe.AI, are expanding capabilities with emotional tone detection and context-aware responses. These tools allow companies to scale communication, all while maintaining a genuinely conversational customer experience and majority of customers do not mind "talking to a robot", as they just want their appointment to be booked and issues quickly resolved.
It doesn't make any sense, maybe for some people it is a pleasant thing, searching for presents for loved ones, why do someone want to give all the fun to AI? Let it do the boring and repetitive tasks, workflow automation for instance!
Totally agree — once a voice agent goes live, it’s hard to see what’s really going on. You only find out things broke after users do. Convolytic sounds like exactly what’s missing — proper visibility and A/B testing for conversational systems. I’d track conversion vs. drop-off reasons, latency vs. engagement, and tone vs. outcome to really understand what drives success. We’ve seen similar challenges while working with Coldi AI (https://coldi.ai/), which builds voice agents for outreach and customer calls — having analytics like this would make tuning and scaling them so much easier.
Sounds like a great product, is there a link? I wonder if Voice AI sounds natural and if buyers realize that they are talking to a robot? We've been checking Coldi for calls automation, which improved the sales situation.
Looks interesting! We've been checking ready call agents from Coldi AI which offers customizable calling agents, can be used in banking sphere as well. Demo here: https://coldi.ai/call-request?botName=Kate
It’s fascinating to see how voice AI is shifting from conversational tools to full task-execution agents. Have experience with ElevenLabs and Vapi, checking Coldi AI for customer outreach.
The question isn’t whether AI can sound human—it already does. The real question is whether we care, as long as the interaction feels authentic, helpful, and kind. In a world where empathy can be automated, the future of customer experience might be not about replacing humans—but about teaching machines to sound a little more like us. For instance, Voice AI has quietly evolved from stiff scripts to emotionally intelligent conversations. Systems like Coldi AI now create voice agents so natural they can comfort a frustrated customer, schedule a demo, or even close a sale—without breaking the illusion of humanity. These agents don’t just “speak”; they listen, interpret mood, and respond with nuance.
We are using AI voice in a customer service for a while and quite happy with results. We understand that AI voice agents aren’t here to replace humans entirely. Customers still want empathy, problem-solving, and sometimes a bit of friendly banter—things machines can’t fully master (yet). But by taking over repetitive, low-value calls, AI allows human teams to focus on building relationships, solving complex issues. Today AI tools are transforming customer service by making calls more efficient and human-like. For instance, they can analyze tone in real-time and handle tasks like appointment setting without needing a full team. One effective approach is using voice agents that integrate with your existing systems. They qualify leads faster, reduce costs, and boost conversions—I've seen reports of 10% more qualified leads. A great example is Coldi.ai, which offers customizable AI agents.