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Former Google, Meta leaders launch Palona AI, bringing personalized, emotive customer agents to non-techie enterprises

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Speaking for myself, interacting with any merchant’s AI-powered chatbot on their website is often an exercise in frustration. Phone trees with robot voices are typically worse. I’d wager I’m hardly alone in my assessment. Who amongst […]

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Speaking for myself, interacting with any merchant’s AI-powered chatbot on their website is often an exercise in frustration. Phone trees with robot voices are typically worse. I’d wager I’m hardly alone in my assessment. Who amongst us hasn’t experienced long hold times, slow responses, lack of updated information and awareness of the customer’s own account history, a granting faux politeness and a host of other inefficiencies?

A new startup called Palona debuted last week that aims to fix this sorry state of affairs. It equips direct-to-consumer enterprises — think pizza shops and electronics vendors — with live, 24/7 customer support sales agents that are uniquely reflective of each business’s brand personality, voice, inventory stock, and value proposition. The electronics vendor has a “wizard” agent made by Palona, while the pizza shop gets a surfer dude agent personality — naturally.

In all cases, Palona focuses on creating AI agents that have high “EQ,” or “emotional intelligence / emotional quotient,” building them from a combination of open source and proprietary AI models and training some of their own using human sociology research.

“A kind of fundamental thesis that the company is that we can create something an experience that is delightful and feels genuine, like a real human conversation,” said Palona co-founder and chief technology officer (CTO) Tim Howes, in an in-person interview with VentureBeat recently. “ChatGPT is a hugely useful tool, but it does not feel like a human conversation.”

Palona claims its system can be easily implemented by a non-techie brand on their website, mobile app or phone lines — with responses uniquely tailored to each brand and each communications environment. And in fact, its agents are already at work handling orders, answering questions and complaints, and suggesting products and upsells to customers.

Strong founding background

In addition to Howes, Palona is co-founded and led by a team of engineers from some of the top tech companies in the world, among them: Maria Zhang, Palona’s CEO, is a former VP of Engineering at Google, VP/GM of AI for Products at Meta, and CTO of Tinder. She also founded Alike, acquired by Yahoo in 2013.

Palona’s chief scientist Steve Liu, PhD, was formerly chief scientist at Samsung AI Center and Tinder. A tenured professor at McGill University, Liu is also a Fellow of IEEE and the Canadian Academy of Engineering, with over 390 research papers to his name.

And Howes himself is the co-inventor of the industry-standard, open source Lightweight Directory Access Protocol (LDAP) online data storage system, as well as co-founder of LoudCloud and OpsWare (the latter acquired by HP for $1.65 billion). He was also previously the chief technology officer at Netscape, HP Software, and led Developer Productivity at Meta AI Infrastructure.

“We’re building fully autonomous sales agents—not tools for salespeople, but actual AI salespeople,” said Zhang in the same live interview, adding that it was “the employee of the century.”

24/7 polite, distinct, personable sales agents

Palona AI positions itself as a solution for companies looking to improve their sales performance, customer engagement, and brand loyalty.

The Palona Agents act as customized virtual sales employees, combining soft sales skills with 24/7 availability, unlimited capacity, and advanced memory recall, and can interact with customers in an online chatbot format, an SMS/text number they can correspond with, or even AI-powered voices over the phone.

“100%—we support voice,” Zhang explained, “For example, in pizza ordering, voice is still a major user pattern. In the Midwest, about 50% of people still call to order. On the East and West Coasts, it’s around 20%, but it’s still significant.”

Palona’s voices are licensed, but the company has the ability to train and deploy custom ones, even voice cloning of authorized customer reps or a CEO, let’s say.

The company realized through testing that the voice version of Palona’s AI sales agents would need to have distinctly different interaction styles from the text chatbot.

“We tested different voice interactions, and for pizza ordering, for example, customers wanted efficiency,” Zhang related. “They didn’t want a chatty AI—they just wanted to get their order done as fast as possible. So we optimized for that, making it have less personality, less verbosity, more efficiency.”

Unlike traditional chatbots that serve as assistants to human representatives, Palona AI is designed to handle entire sales cycles without human intervention.

“There’s a big gap between lifelike AI models like ChatGPT and what businesses actually need—an AI agent that can fully sell, convert, and upsell,” Zhang explained.

Palona claims to minimize errors and reduces AI hallucinations by up to 98%, ensuring reliable interactions.

Zhang and Howes said that for even the most analog businesses, it was a short lead team to get going with Palona, only several days for a simple implementation.

The enterprise provides Palona with “FAQs, employee training manuals, policies, and procedures,” said Howes.

Then, they define with Palona what actions the agent should take — be it processing orders, answering inquiries, or handling support issues.

“The biggest factors affecting setup time are: how much integration is needed with the customer’s existing systems (e.g., POS, CRM, ordering platforms). If we already support their system, it’s plug-and-play,” Howes explained. “If it’s a system we already support, the agent can be ready in a couple of days. If they’re using a new, unfamiliar system, that requires additional engineering work, which could take longer.”

In addition, Zhang said that Palona was “actually in the process of automating agent setup. Eventually, businesses will be able to use a Palona agent to configure their own Palona agent!”

Three language models are better than one

Palona does all this by combining three different models: the first is a custom, fine-tuned large language model (LLM) that serves as the basis for every distinct business sales agent — the pizza shop gets a different tone and personality from the electronics vendor, and each one is customized to each customer out of the box.

There’s also a supervisory model that detects, catches, and removes hallucinations from the main model before it outputs them to the customer.

Finally, the system also incorporates a real-time memory tracking small language model (SLM), allowing it to build deep customer profiles based on previous interactions—leading to personalized conversations and stronger customer relationships.

“AI is very good at either remembering absolutely everything, which leads to a terrible experience,” Howes said, due to it bringing up unimportant details. “Or it remembers nothing, which, again, terrible experience,” because it won’t know what happened previously in the conversation.

To get around this, Palona trained its own small model to help its base LLM manage its memory.

It’s that small model’s “job to figure out, ‘okay, what’s important here, what’s not important,’” in every conversation, Howes said.

Overall, the company included an interesting dataset for all its models that reflects the wide range of human emotional responses all sales and customer-facing humans (and agents) have to deal with on a regular basis.

“We trained our models to have higher EQ [emotional intelligence] by surveying the literature in psychology, and identifying what actually makes someone emotionally intelligent,” Howes told me. “It turns out there’s an eight-dimensional definition of EQ, and we trained our AI against those benchmarks.”

Palona also trained its models to perform “gentle persuasion,” and reply to customers with humor, emoji, and sensitivity.

Early customer testimonials: Wyze, MINDZERO, and Pizza My Heart

Palona AI is already working with several consumer brands, including Wyze, MINDZERO, and Pizza My Heart, to enhance customer interactions and increase sales conversions.

Wyze, a smart home camera company, has integrated Palona-powered AI sales assistants to personalize customer support.

Yun Zhang, CEO of Wyze, said in a statement: “I’ve always wished that I could personally connect with every customer to share why they should choose our camera and CamPlus plan. Palona has made that possible with the Wyze Wizard AI Agent, delivering the curated, personalized buying journey I wish I could give every customer myself.”

MINDZERO, a wellness studio specializing in contrast therapy, uses Palona AI Agents to answer customer questions naturally and engagingly. David Semerad, CEO of MINDZERO, highlighted the impact of the technology: “Our Palona agent, Jen, has become our new best friend—she’s able to answer customer questions in such an authentic, kind, and helpful way, it really blows our minds.”

Pizza My Heart, a well-known West Coast pizza chain, has transformed its brand persona into an AI-powered interactive experience. The company’s Palona AI Agent, “Jimmy the Surfer”, allows customers to order pizza through voice or text while engaging in lighthearted conversations.

Chuck Hammers, CEO of Pizza My Heart, commented on the experience: “Palona AI brought Jimmy to life in a way that just makes me smile—which I never expected from an AI. Now, Jimmy helps my customers order pizza by talking to them directly—via voice or text.”

Three pillars of Palona’s success

Palona’s AI solutions are built on three core principles:

1. Brand Consistency – AI Agents are trained on a company’s knowledge base to ensure conversations remain true to the brand’s voice and identity.

2. High EQ (Emotional Intelligence) – Palona Agents dynamically adapt to conversations and outperform both human representatives and competing AI models in emotional engagement.

3. Persuasiveness – The AI identifies key sales opportunities, proactively recommending upsells and cross-sells at the right moments to increase revenue and customer lifetime value (LTV).

I saw all these in action in a short demo of a “Wizard” customer sales chatbot agent, in which even when I complained to it about my dogs barking at my neighbors (sorry guys!), it managed to smoothly redirect the conversation to a home camera I could buy to help keep an eye on them.

What’s next for Palona?

“We intentionally went after the hardest problem first—sales and conversion,” Zhang noted. “It requires more technical sophistication, but if you can solve that, expanding into other areas like customer support and loyalty programs is easier.”

As AI continues to shape the future of commerce, Palona’s approach suggests that customer relationships don’t have to be sacrificed for automation—instead, AI can enhance personalization, drive conversions, and build lasting brand loyalty.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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