Latest AI News
Omilia raises $67M to scale its customer support platform
The customer support industry has seen a massive influx of startups like Sierra, Decagon, and Parloa trying to infuse AI into automating customer calls, chat, and messages so companies can handle more queries at scale. But according to Athens-basedOmilia, which has been working on automating voice calls and customer support since 2002, throwing AI at every process can be wasteful. The company’s CEO, Dimitris Vassos, says that a large proportion of incoming customer support queries involve basic information, such as account balances, and there’s little need to deploy large language models for such tasks. “We will use any available weapon to win the battle for customer service. Companies like Sierra, Decagon, etc. identify as generative AI companies. Their sole purpose is to deploy generative AI and limit themselves. You may have a bazooka, but if your enemy is near you, you need a knife. This is the reality of the contact center, where you need multiple tools,” he said. Omilia itself has expanded its operations to building self-learning agents that can work across different customer contact points. To build that out, the company has now raised a $67 million Series B led by Expedition Growth Capital. This round is the company’s second time raising capitalafter it raised $20 million from Grafton Capital in 2020. Since then, it has managed to increase its annual recurring revenue by 10x to $60 million. Vassos says Omilia’s key differentiation lies in having great unit economics for both itself and its customers, and thanks to this approach, it hasn’t needed large cash infusions as compared to its competitors. “In the next few years, we will see the companies that can offer real ROI prevail. We don’t mind that we’re not as sexy as ElevenLabs and Sierra on LinkedIn right now. We care about growing steadily and building the foundations for a billion-dollar revenue company in the next three years,” Vassos said. Omilia’s clients include Capital One, Discover, RBC, DWP, and PSEG, and Vassos said quick-service restaurants that have adopted voice-based ordering are now a big focus. The company already has Taco Bell as a client and has deployed its tech across more than 1,000 outlets. Vassos said that it is in talks with two more quick-service restaurants in the U.S. But AI deployments can be quite hit or miss. In Taco Bell’s case, a snafu in its ordering system reportedly allowed a customer to order18,000 cups of water last year. Vassos maintains this incident never took place, and Omilia’s logs didn’t show it. We have reached out to Taco Bell to clarify, and we will update our story if we hear back. Omilia will use the fresh cash to open a new office in the U.S., a market that accounts for a significant chunk of its revenue. It will also bolster its go-to-market team and is hiring a chief revenue officer, chief marketing officer, and a VP of revenue operations. The company has around 500 employees at the moment and expects its headcount to reach 600 by the end of the year.
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Google Maps adds agentic features, including food ordering and hotel bookings
Google announced on Thursday that Google Maps’ “Ask Maps” feature is gaining a slew of new agentic capabilities, including the ability to order food, book hotels, and find event tickets. The tech giant is also bringingPersonal Intelligenceto Ask Maps, allowing the AI tool to personalize its responses by drawing on information from a user’s Gmail and Google Calendar. The launch of the new features reflects Google’s ambitions to transform Google Maps from a navigation tool into an assistant that’s capable of helping users complete real-world tasks. With the new agentic food-ordering capabilities, users can open Ask Maps in Google Maps and ask a question like, “Where can I order vegan avocado toast and an oat milk latte near home?” The tool will then surface nearby restaurants that have what they’re looking for. After selecting a restaurant, users can tap the “Order online” option to place an order through supported platforms such as Square, Toast, or Uber Eats. Ask Maps will add the items to a cart. Users can choose to add more items to their cart, then pay for the order on the supported platform. Additionally, users can use Ask Maps to find a hotel for their next trip by entering a query like, “For next weekend’s conference in downtown Miami, find me a decently priced, top-rated hotel with an artsy vibe within walking distance from a gym and restaurants.” Ask Maps will then compare prices and check availability before providing a list of options. Once users find an option they like, they can click through to the partner’s website to book the stay. Users can also ask questions like “What are some comedy shows or live music this evening near work?” to get a list of options along with links to buy tickets. The new agentic features are rolling out to users in the U.S. With the addition of Personal Intelligence, Ask Maps will be able to give users personalized answers to their questions about upcoming flights, dinner reservations, hotels, and more. For example, users could ask, “What time will I land in Vancouver on my upcoming flight?” or “Can you recommend where to eat and what to do near my hotel?” Ask Maps will then pull information from a user’s emails and calendar to provide tailored responses. It’s worth noting that Personal Intelligence is off by default. Ask Maps will now also remember prior conversations, allowing users to pick up where they left off. Now users can ask things like, “What activities had you suggested for my trip to Seattle?” and continue planning without having to start from scratch. Google is also launching a live transit widget in Ask Maps that will allow users to stay up to date on delays and conditions as they change in real time. Personal Intelligence and the live transit widget in Ask Maps are rolling out to all markets where Ask Maps is available.
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Exclusive: Mirendil inks $100M+ Google Cloud deal to scale self-improving AI
AI labMirendilhas signed a multi-year partnership with Google Cloud to source compute capacity for its self-improving AI research, TechCrunch has exclusively learned. The deal mirrors two trends shaping the AI industry: cloud giants are courting startups with huge infrastructure commitments, and AI companies are snatching up as many compute deals as they can to secure access as they scale. The deal is worth upwards of $100 million, Mirendil’s co-founder and CEO, Behnam Neyshabur, told TechCrunch. That’s roughly half of what Mirendil raised inseed funding at a $1 billion valuationin late June. The deal gives the startup access to both Google’s TPUs and Nvidia GPUs, as well as managed training clusters with which Mirendil will work on its self-improving AI. The startup hopes its AI will eventually be able to take on the work of an entire frontier AI lab. Self-improving AI, also known asrecursive self-improvement, refers to AI systems that iteratively improve themselves. It’s a concept that major labs likeAnthropic, where Mirendil’s co-founders hail from, have been working on. A handful of startups likeRecursive SuperintelligenceandRicursive Intelligencehave also recently sprung up around achieving that goal. Mirendil believes this process will automate a lot of scientific and AI research, helping scientists make progress in fields like medicine, biology, and materials science. Neyshabur thinks AI can mimic how human scientists can learn more about new domains, accumulate knowledge and expertise, and gradually improve their performance. “You can have a self-improving AI where you can point a problem at it and it keeps getting better with time,” he said. “How can we have an AI system that keeps doing research, keeps improving its own knowledge and performance when it comes to Alzheimer’s disease?” he continued. “This technology allows us to set goals that are ambitious for AI, and the AI would keep making progress.” Training self-improving AI, however, requires enormous amounts of computing power. The lab’s co-founder Harsh Mehta said training is increasingly about matching the right workloads to the right hardware. “These models are really good at working with different workloads and chips, and assigning the right workloads to the right chips,” Mehta said. “[Google] provides multiple kinds of chips […] this flexibility allows us to ultimately mix and match workloads with the right kind of accelerators, and then lower the cost not just for us, but also for our customers using our systems.” That flexibility is central to Google’s AI infrastructure pitch. Amin Vahdat, SVP and chief technologist of AI and infrastructure at Google, said in a statement that AI advancement isn’t just about chip-level performance anymore, “but how we orchestrate entire systems of intelligence and break through the physical constraints of scaling.” Neyshabur said Mirendil’s software and systems layer help customers get more out of Google’s hardware, giving the cloud giant another potential leg up in the race against its competition. In return, Google gets a strategic partner building frontier recursive self-improving AI — technology that it can eventually shop around to enterprise customers.
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Ex-Spotify employees raise $10M to bring the AI behind its recommendations to e-commerce
Sidd Motwani, Ian Anderson and Shivaditya Sinha spent years building the behavioral intelligence infrastructure behind Spotify’s recommendation engine. Called Vector AI, the system is designed to predict a person’s intent and next actions instead of relying only on their past behavior. It powers about 90% of Spotify’s recommendations to its 800 million users. Now, the three are bringing a similar system to e-commerce with their new startup,Malachyte. The company on Thursday said it had raised $10 million in seed funding to scale distribution and hire more product and commercial leaders. Malachyte was formed from the belief that most online stores treat shoppers the same way: Personalization is largely dictated by historical purchases, demographic segmentation, or logged-in customer profiles. That means first-time visitors often see the same generic storefront as everyone else, while existing shoppers receive recommendations based primarily on what they bought previously rather than what they need today. The startup wants to change that by building real-time, intent-aware shopping experiences. Its platform uses what it calls “two-headed Vector AI” to predict what product a shopper wants next, learn their general taste, and fine-tune continuously based on what they do in real time. “[Our] system starts forming before the first click, using the context available the moment the page loads. Within a single session, we build a real read on both preferences and what someone is trying to accomplish right now,” Motwani, Malachyte’s CEO, told TechCrunch. “A search for ‘heavy-duty boot’ followed by two clicks on steel-toed boots is enough to move work pants and gloves up the page and push dress shoes down, with no account or history required. Every additional action sharpens the profile, so the experience gets more relevant the longer someone stays, and again on their next visit.” Motwani argues that retailers already possess their most valuable source of customer intelligence, but rarely take advantage of it in real time. “Every hover, click, scroll, search refinement and add-to-cart is a signal, and most systems either never act on it in the moment or aggregate it into a segment overnight. We read it continuously, so each action makes the user’s vector more confident about both preference and current intent,” he added. He also believes contextual signals remain significantly underutilized. “A phone visitor at 11 p.m. from an email link is in a different state of mind than the same person on a laptop mid-morning, and most systems treat them identically,” Motwani said. The company has been developing and testing its technology since 2024, and worked with more than 20 enterprise customers across travel, grocery and retail before ultimately focusing on e-commerce. Its platform first went live in the fall of 2025 with Fun.com. Since June 2026, it has been generally available to Shopify merchants through a native integration, while larger retailers can integrate the technology through its API. Looking ahead, Motwani says the bigger opportunity is in bringing merchandising and marketing together around the same understanding of customer behavior. The funding round was co-led by Bessemer Venture Partners and Gradient, with participation from Harpoon Ventures.
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Amid legal battles, Suno says it will start watermarking songs
Suno, the service that allows users to create AI-generated songs, today announced new tools to mark tracks made on its platform, limit downloads, and update community guidelines to prevent copycat songs. The changes come asSunofaces numerous lawsuits from labels andartist bodies. Ina blog post, co-founder and CEO Mikey Shulman shared core principles and said that the platform wants to promote original creation while enabling more people to make music with its AI tools. One of the key points of contention involved users uploading AI-generated songs on other streaming platforms and gaming the system to earn revenue. Suno said that now it will use audio watermarking and fingerprinting to prevent misuse on other streaming platforms. It’s not clear if Suno will use an existing system likeGoogle’s Synth IDor adopt a new one, and the company did not say when contacted by TechCrunch about this. The startup also signed an agreement with lyrics provider Musixmatch to use its Sentinel system for copyright detection, the blog post said. “These tools are designed to be durable and resistant to tampering, without affecting the listening experience. They are also not intended to pass judgment on whether a song is good, meaningful, or sufficiently human,” said Shulman in the blog. “Ultimately, we believe it should be up to artists and platforms to decide what they want to disclose. Our role is to build tools that give them transparency options and make it easier to collaborate across the industry.” The company added that it plans to add a new download policy to bar mass distribution on streaming platforms, but declined to provide details on the record. Suno has also changed itscommunity guidelinesto explicitly prohibit “deceptive audio presented as real” and “using a real person’s voice or likeness without permission” to prevent copycats. The company, which raised$400 million in a Series D funding round in June, is fighting legal battles on many fronts. The startup is in a lawsuit withthe Universal Music Group (UMG) and Sony Music Groupin a case coordinated by the Recording Industry Association of America (RIAA). Late last month, a German court ruled in favor of a government-mandated licensing agency, GEMA, and saidSuno was breaking copyright rules. Separately, 404 Media reported thatSuno experienced a data breachin November 2025, which revealed that the platform scraped YouTube, Deezer, and Genius to train its models. Later, data breach notification service Have I Been Pwned said thatthe data breach affected 55 million users. The startup now faces a class action lawsuit in Massachusetts, which alleges thatthe company overlooked security measures to focus on profit.
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OpenAI says Apple’s own security practices undermine its trade secrets case
OpenAI’s motion to dismiss Apple’s trade secrets lawsuit, along with newly filed exhibits, reveals the company’s legal defense strategy. Rather than focusing on whether former Apple employees working at OpenAI had accessed certain information, the AI company argues that Apple’s own security practices and offboarding procedures weaken its claim that the information qualifies as legally protected “trade secrets.” Apple’s complaint,filed in July, accuses OpenAI of orchestrating a scheme to obtain confidential hardware information from former Apple engineers. This week, Appleasked the court to expedite discovery, saying its internal investigation indicates some additional former employees may have participated in, or witnessed, the alleged theft of trade secrets. In its motion, OpenAI argues that Apple allowed employees to use personal iCloud accounts for work and failed to properly revoke access after they left the company. It also submitted text message records showing that an Apple manager remained logged into the personal iCloud account of defendant and former Apple engineer Chang Liu after he left the company to transfer files, and later asked him for help with technical questions about Apple projects. OpenAI also accuses Apple of omitting the consequences of its own “inexplicable information-management practices” from its original complaint, noting that the company failed to properly secure its systems when employees departed, creating confusion and unwanted access issues that Apple now characterizes as theft. While this may sound a bit like a “the door was unlocked, so it wasn’t really stealing!” defense, this argument is meant to strengthen OpenAI’s argument that former Apple employees were simply trying to assist their prior colleagues. It could also help push the narrative that these weren’t truly trade secrets if they weren’t secured as such. OpenAI argues Apple hasn’t specified which “trade secrets” or confidential components were allegedly stolen, referring to them instead as “generic categories of the product-development process—such as component manufacturing, product testing, vendor and supplier relationships, and distribution channels.” OpenAI’s filing posits that Apple is using this lawsuit to slow down its competitor from innovating in AI-powered hardware, rather than focusing on its own product plans. “OpenAI has no use, need or desire for Apple’s trade secrets,” the motion argues. “OpenAI is building something entirely new and different from anything at Apple. OpenAI does have an interest in hiring the best engineers, inventors, developers and creators—many of whom have decided to leave Apple and to come to OpenAI, attracted by the innovative and exciting work the company is doing. Apple might not like that,” it reads. “…Apple should not be permitted to use a baseless and pretextual lawsuit to make up for itsshortcomings in the market for talent and retaining its employees, and its failures to integrate AI into its products,” the motion says.
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Schreiber Foods Taps Ascendion to Deploy Agentic AI Across Global Business
The partnership will introduce AI agents to automate technology workflows, improve food safety monitoring and modernise ERP systems across its global network.
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Microsoft Launches Hyderabad Cloud Region Amid AI Demand
Microsoft’s Hyderabad region has already attracted customers including Adani Group, Bajaj Finserv, HDFC Bank, and PB Pay while strengthening its AI and cloud infrastructure.
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KDEM & GoodWorks Target ₹5,000 Crore GCC Investments, 50 New Centres in Karnataka
The collaboration also places significant emphasis on workforce development.
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Powered by Intel, SoftBank Plays the OpenAI Long Game
Despite a $20 billion OpenAI investment in the last few months, it was Intel that delivered the biggest boost to SoftBank’s quarterly investment gains.
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Karnataka Eyes Anthropic Partnership to Bring AI Into Public Services
The discussions included plans for an AI University, Claude certification programmes and hosting government data within Karnataka.
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Anthropic Could Build Custom AI Chips for Claude Models, Job Listing Suggests
Anthropic recently relaunched its most capable AI model yet, Claude Mythos, which offers cybersecurity capabilities, along with Claude Fable 5. With the rising demand for AI models, the US-based AI giant could be looking to foray into a new category, as it has started hiring engineers for its Reinforcement Learning team, which will be responsible for designing custom AI chips in-house. A company spokesperson has reportedly confirmed the development as well. The AI chips are said to enhance the performance of Claude AI models. Anthropic is also reportedly in talks with Samsung about manufacturing its chips.
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