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Exclusive: From Booking Calls To Late Check-Ins, Dextr AI Raises $6.7M For Hotel AI Agents

View original at news.crunchbase.com
Crunchbase News - Funding Ma Title: Exclusive: From Booking Calls To Late Check-Ins, Dextr AI Raises $6.7M For Hotel AI Agents Date: 2026-09-24 13:00 Source: https://news.crunchbase.com/venture/dextr-ai-hospitality-agents-raises-seed-funding/ <p>When <a href="https://www.crunchbase.com/person/sajid-shariff">Sajid Shari…
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  • Sajid and Scott's execution stood out; Dextr scaled to hundreds of contracted properties and meaningful ARR without raising a single dollar prior to this round

    60% confidence
  • It was very hard to hire for overnight front desk shifts

    60% confidence
  • Online reviews improved at both initial properties within 90 days of deploying the Alfred agent

    60% confidence
  • Connecting multiple agent use cases is important to achieving higher ROI for hotel properties

    60% confidence
  • Dextr's agents now handle more than 1 million interactions a month across hundreds of hotels

    60% confidence
  • Dextr's annual recurring revenue has been growing about 20% to 30% month over month

    60% confidence
  • Staffing an overnight front desk shift could cost a property $70,000 to $80,000 a year in a market such as California

    60% confidence
  • In a space where most AI companies are still searching for product-market fit, Dextr had customers, zero churn, and a clear ROI story, which matches what Elevation Capital looks for

    60% confidence
  • One large hotel that Dextr works with handles $100,000 to $300,000 a month in bookings through the voice agent Daisy

    60% confidence

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High probability of expanded label indications, additional combination approvals, and competitive positioning strength in follicular lymphoma market. Predicts positive commercial uptake and potential accelerated review for related indications.
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Ford Motor Company
Both facts give cash for Ford Motor Company at the same point in time (2025-12-31; FY 2025 year-end and Q4 2025 year-end are the same date). Both are labelled USD, but the values are 23,356,000,000 and 23.356, which cannot both be true in USD. The two numbers agree once the second is read as billions (23.356 bn = 23,356,000,000), so the economic content is consistent and the conflict is a unit/scale error in the Q4 2025 record. The period is not the reason for the conflict: a Q4 and an FY figure at the same year-end date should match.
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