Ellis AI announced Thursday its emergence from stealth with $10 million in seed funding from investors including First Round Capital, 645 Ventures, Harlem Capital, Khosla Ventures, Thrive Capital, Slow Capital, Kearny Jackson, and Ariel Alternatives CEO Mellody Hobson.

Ellis uses AI agents to tackle the fragmented workflow private credit managers deal with, including managing documents, spreadsheets, and correspondence. The company was founded by Ryan Williams, best known for co-creating the real estate investment platform Cadre alongside Josh and Jared Kushner back in 2014. That company raised more than $160 million in funding and, at its peak, was valued at $800 million before being sold for an undisclosed sum to the alternative investment company Yieldstreet in 2024.

“At Cadre, I saw the next major constraint,” Williams said. “Even as the front end of private markets became more modern and accessible, the operating infrastructure underneath it remained fragmented.”

He started working on Ellis last year. The company seeks to connect and centralize all the scattered software, accounting information, and documents a private credit firm would use into one easily accessible platform. The system can flag discrepancies in the data and uses AI agents to help perform tasks like portfolio monitoring and preparing reports.

For example, Williams promises the agents can help close a fund’s books at the end of the month.

“A team may have to download files from several systems, reformat the data, compare balances, investigate discrepancies, and re-enter information by hand. In many firms, Excel becomes the operating system,” he continued. “Ellis connects to the systems and documents a firm already uses rather than forcing it to rip everything out and start over.”

It keeps a human in the loop, too, he says. “Material decisions and actions remain with the human experts,” he said.

“I expect the human loop to become narrower, but not disappear,” he continued, when asked if he sees a day when the AI works fully autonomously. “Our goal is not to replace human judgment; it’s to help people cut through the noise and make educated decisions faster.” 

This piece was updated to add an investor.

During Meta’s Q2 2026 earnings call, Mark Zuckerberg said that other revenue in the family of apps segment crossed $1 billion, largely thanks to WhatsApp paid messaging and subscriptions.

As more businesses use WhatsApp to reach consumers, users’ inboxes often get cluttered, making it hard to find personal and group messages. Meta is now trying out a new feature where it will place messages from larger businesses like banks or airlines in a separate folder, TechCrunch has learned exclusively.

When a user receives a message from a large business, WhatsApp will automatically move that message to a new “Offers & Updates” folder after a set number of hours. The company said it is testing different durations, up to 24 hours, to move messages to a new folder.

Users who prefer their messages to be on the timeline can turn this setting off. However, they don’t control when messages are moved automatically.

Meta said that with this feature, messages like discount codes and delivery updates are out of the inbox in a few hours, and the main chat timeline feels less cluttered. For businesses, this means that users can look for their messages in a specific folder rather than getting lost in all chats.

WhatsApp is starting to test this feature with select partners using its WhatsApp Business Platform, and will look to expand based on observations. At the moment, small businesses and individual accounts using WhatsApp Business are exempt from this feature. WhatsApp said it could explore moving business messages from small businesses to the new “Offers & Updates” folder in the future.

In the last few years, WhatsApp has taken steps to reduce business message spam. In 2024, it started allowing users to unsubscribe from marketing messages from brands. Last year, it put a curb on the number of broadcast messages businesses and individuals can send in a time frame. In October 2025, it went one step further and limited the number of messages businesses could send without getting a response from users. The company has fully rolled out the first two features while it is still iterating on the third feature.

Despite these steps, the WhatsApp inbox can feel chaotic. From my own experience, there have been days when I have cleared unread messages at the start of the day only to end with more than 30-40 unread messages. Even at the time of writing, more than half of my unread messages were business communications. I am not alone in feeling this.

The new feature might reduce the clutter a little, but it won’t be effective until users have control over filtering out messages from the main inbox.

WhatsApp made its AI business agents available globally in June, with more than 1 million businesses already using them. During the earnings call, Zuckerberg mentioned Brazil’s car rental company Movida and said that it has seen an uptick in conversions and customer support issue handling through AI agents. In the coming months, we could see more businesses use AI within WhatsApp for sales, marketing, and support use cases. A chat app with over 3 billion users must strike a balance between personal and business messages before it becomes a vehicle for AI spam.

While AI agents are increasingly capable of solving customer support problems, most people can still tell immediately when they’re talking to a machine instead of a human.

Smallest.ai, a startup founded in late 2024, is betting the next leap in voice agents will not come from making large language models faster, but from using smaller, specialized models built for human conversation. Simply put, the company wants to make speaking to an AI agent indistinguishable from talking to a human.

To do so, it’s developing a small voice model designed to mimic how humans process information by listening, thinking, and speaking simultaneously.

“While I’m speaking to you, you’re already thinking, and you might interrupt me if I talk for too long,” Sudarshan Kamath (pictured left), founder and CEO of Smallest.ai, told TechCrunch, adding that this is exactly how the startup’s model is designed to work.

To fuel this mission, Smallest.ai has raised $13 million in a Series A round, led by Seligman Ventures with participation from Sierra Ventures and 3one4 Capital. The fresh capital brings the startup’s total funding to over $21 million.

“The way an LLM works is you give it an entire prompt, and then it starts thinking,” Kamath said. While that latency is acceptable in a text chat, in a voice conversation, even a short pause feels unnatural. “If you think about how we are talking, I’m not giving you like a large clipping of my audio, and then you start thinking.”

The startup’s model serves as a real-time intelligence layer that enables natural customer conversations on specific topics, with virtually zero response lag. But if the model encounters a subject outside its limited knowledge base, Smallest.ai hands off the query to a large foundational model, briefly placing the customer on hold to “research” the issue — just as a real human would do.

Kamath believes that all AI agents will soon rely on two models: a small voice model for real-time interaction, and an “offline” LLM that is called upon as needed to solve complex problems.

Unlike large foundational models, Smallest.ai focuses strictly on voice-specific nuances, such as handling diverse accents, supporting dozens of languages, and operating in noisy environments.

The startup’s existing customers include companies in the voice space, including RingCentral and Truecaller. Kamath said that any customer support company, including newer ones like Sierra and Decagon, is a potential customer for the startup.  

When asked why a well-funded AI customer support company wouldn’t build its own voice model, Kamath said that for customer support startups, becoming “extremely good at doing voice is a distraction from their core business.”

Smallest.ai competes with voice AI leader ElevenLabs, as well as Cartesia and regional players like Sarvam that focus on local languages.

While some competitors apply voice AI to use cases, like audio dubbing and podcasting, Smallest.ai focuses strictly on real-time conversational voice agents for its enterprise customers.

“We want our models to break the Turing test,” Kamath said. “You should speak to our model and not know it’s AI or human. That’s the sole focus of the company.”