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When returned to the Bay Area in 2024, he reconnected with people he knew from his years working in hospitality. He asked what had changed since he left the industry during the pandemic. The answer, he said, was that hotels were still challenged by rising labor costs, staffing gaps and pressure to bring in more revenue.

So Shariff offered to build an AI agent for a couple of properties. The first, called Alfred, answered guest questions and helped with check-ins and check-outs. Within 90 days, he said, online reviews improved at both properties. He then began deploying it at more hotels, before deciding that guest messaging was only one part of the opportunity.

That work led him to team up with to start , the San Francisco startup they founded in July 2025. The company is now emerging from stealth with $6.7 million in seed funding to build agents that handle reservations, guest requests, staff coordination and other hotel tasks. led the raise, with participation from . The round marks Dextr鈥檚 first institutional financing.

From guest requests to bookings

After early guest management deployments, Dextr turned to incoming calls with a voice reservations agent called Daisy. If a caller has a question about a reservation or something they can’t find on a booking site, answering it could lead to a booking, Shariff found. As hotels began using Daisy, he realized that the phone calls were 鈥渓eading to revenue.鈥�

In fact, today, one large hotel that Dextr works with handles $100,000 to $300,000 a month in bookings through the voice agent, according to Shariff.

Dextr has since expanded into agents for group bookings, staff management and other work. Its goal is to connect those agents so information gathered in one part of a hotel can prompt action in another. For example, if a guest declines housekeeping, a guest management agent could pass that information to an operations agent so that staff schedules could be adjusted accordingly.

Another use case could involve a property operating without an overnight front desk employee. A guest arriving late could call the hotel, have Daisy verify their reservation and receive instructions for entering their room. Daisy and Alfred, Dextr鈥檚 guest management agent, would work together to handle the arrival.

For some properties, using agents to handle late check-ins could remove the need to staff an overnight front desk shift. Shariff estimated that such a shift could cost a property from $70,000 to $80,000 a year in a market such as California.

鈥淚t was very hard to hire for these shifts to begin with,鈥� he said.

Building ROI

Shariff says combining agents is just one of three things that set Dextr apart from other hotel AI tools. An agent that answers calls or messages can handle a particular task, he said, but connecting it with other agents opens up additional uses for a property.

鈥淚t’s important that we do multiple use cases to see the higher ROI,鈥� he told 黑料吃瓜 News in an interview.

The second distinction, according to Shariff, is Dextr鈥檚 approach to getting the agents running. Putting them to work takes more than installing software, he said. Every hotel differs from another in how it manages reservations, assigns staff, and handles guest requests. So Dextr sends engineers to work directly with customers, identify where agents could be useful and then connect them to existing systems. Those engineers stay involved as the property puts the agents to use, he said, and work to find applications that have the potential to deliver a return on investment.

鈥淓very property, every operation is different in this space,鈥� he said. Dextr integrates with property management systems including Oracle Hospitality, OPERA Cloud, , and .

The third is how employees use the agents. They can direct them by text or voice through a companion called Doss, without having to learn a series of steps in a new software system. Shariff said frequent turnover makes it difficult for hotels to train staff on complicated tools and retain that knowledge when employees leave. With Doss, 鈥測ou can call it, you can talk to it, you can text it,鈥� he said.

Hotels and more

Dextr says its agents now handle more than 1 million interactions a month across hundreds of hotels. The startup鈥檚 customers range from a 21-room property to one with 550 rooms. They include independent hotels and properties operating under the , , and brands, as well as reservation centers, vacation rentals and outdoor hospitality businesses.

The startup works directly with the owners and operators of those branded properties, Shariff said, and is beginning its first direct relationship with a hotel brand that he declined to name. It also works with hotel and property management companies, as well as reservation centers, vacation rentals and outdoor hospitality venues such as campgrounds, RV parks, lodges and golf courses.

The company charges a subscription based on the property and the agents it uses, with usage-based fees for some agents. Shariff declined to disclose revenue, profitability or the round鈥檚 valuation. After spending much of last year on pilot projects, he said, Dextr began growing more quickly this spring. He said annual recurring revenue has been growing about 20% to 30% month over month, though he did not disclose hard figures.

Dextr plans to use its new funding to hire more engineers who work directly with customers, expand its software integrations, and develop additional agents. Its team has grown from three people a year ago to 21.

, an AI partner at Elevation Capital, told 黑料吃瓜 News that Dextr鈥檚 early traction got the firm鈥檚 attention. He pointed to customers using the agents to generate bookings and upsells alongside reducing operating costs.

鈥淲hat stood out immediately was Sajid and Scott’s execution. Dextr scaled to [hundreds of] contracted properties and meaningful ARR without raising a single dollar,鈥� he wrote via email. 鈥淚n a space where most AI companies are still searching for product-market fit, Dextr had customers, zero churn, and a clear ROI story. That combination of founder quality and early traction is exactly what we look for at Elevation.鈥�

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