4 MIT Sloan startups solving logistics problems in industry and health care

Kristina DeMichele

May 6, 2026 3 minute read

The MIT Startup Exchange supports MIT-connected ventures as they explore and assess new technologies. Four startups led by MIT Sloan School of Management alumni were featured as part of a recent virtual Demo Day. See what each of these startups is doing to make other firms work better.

Oddness

Market Pain Point: Manual work still drives the logistics market, and the adoption of automation in the industry remains slow, costly, and difficult to scale.

Solution: Oddness uses a combination of artificial intelligence and robotics to improve operations within the logistics market. The company has two products ready for real-world deployment.

OddiCheck: An AI system that validates pallet contents as they’re loaded for shipment
OddiStock: A robot that maps current inventory stored on pallets

How It Works: OddiCheck equips every pallet with GPU cameras and depth cameras that are connected to a tablet. OddiCheck then determines which products need to be picked and in which order. Oddness co-founder Ariel Schilkrut, PhD ’01, said that the company has been working to implement OddiCheck at Coca-Cola Andina, Kuehne+Nagel, and Cencosud.

The OddiStock inventory management robot has a high mast and is equipped with cameras and RFID readers to move around a warehouse and find the exact location of every pallet. Oddness has been working to implement OddiStock at Ikea and Aramark.

Silvis Materials

Market Pain Point: Adhesives for packaging and building materials often contain materials derived from fossil fuels. Bio-based adhesive options exist, but they often perform poorly, are costly (sometimes 100 times the price of fossil fuel-based adhesives), or have limited applications.

Solution: Silvis, whose leadership team includes MIT Sloan graduates Shereen Shermak as CEO and Patty Ferreira as COO, manufactures biodegradable, compostable, and recyclable adhesives.

How It Works: Silvis sources cellulose from biomass and tree sources and claims to decarbonize building products by up to 80% and packaging by up to 40%, compared to standard adhesives.

Owle AI

Market Pain Point: Over 5 million Americans receive post-acute, long-term, or rehabilitation care outside of hospitals. There are single-purpose tools that generate patient alerts, but caregivers are already overwhelmed with alarm fatigue.

Solution: Owle AI is an AI agent system designed for post-acute care facilities that detects early warning signals at the bedside in real time. The system prioritizes and executes the right task to the right caregiver upon approval; handles paperwork, follow-ups, and compliance; and optimizes staff allocation.

How It Works: Founder and CEO Breana Patel, EMBA ’24, presented an example scenario: In room 214B, Owle AI presents critical patient vitals — low oxygen, high blood pressure, increased cardiac/escalation risk — to a nurse. The nurse confirms the information, and then AI agents manage follow-up and compliance paperwork. Owle AI completed a 10-week pilot program at a 150-bed New York facility, which saved 300 clinician hours and more than $56,000, Patel said.

Cosito

Market Pain Point: Many front-line workers in factories, warehouses, and other industrial environments still log data manually using paper, spreadsheets, or complex, nonintuitive enterprise systems. This practice leads to low-quality data, slow operations, and systemic risk.

Solution: Cosito, led by MIT Sloan alumna Andrea Diaz Baquero, enables voice-first operational data capture for front-line workers.

How It Works: Cosito transforms spoken operational data (e.g., “Machine 12 needs oil; it’s vibrating” or “25 light bulbs, 10 electricity cords”) into structured data sent via APIs to business applications from firms like Oracle, Microsoft, and Redzone. Cosito also provides insights such as hours of work saved per year, number of workdays reclaimed, and projected labor cost savings per year. A pilot with Nuchas Handheld Foods, a food manufacturer, reduced the time staff spent logging 170-plus daily data points (related to temperature, texture, taste, appearance) by 70%, saving each employee two to three hours per shift, Diaz Baquero said.

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