How it worksThe damage your team missed, caught automatically Damage detectionAI compares turnover photos against baseline Video walkthroughsFull-room coverage from a phone walk-through Breezeway integrationPlug into the turnover workflow you already run Chrome extensionOne-click upload from any tab Turnover checkTry it free on one property, no setup
New Cleaner Verification just shipped Changelog
Vacation rental managersCatch guest damage between back-to-back turns Hotels & resortsRoom-ready verification at housekeeping scale Large portfolios200+ units, one consistent standard Cleaners & field teamsProof the work was done, the first time
One engine, every kind of property View all industries
Content libraryEverything we know about turnover ops BlogGuides, comparisons, and deep dives Open datasetsFree STR-ops data, cited by anyone ToolsCalculators and free utilities ShowcaseReal damage RapidEye caught AboutWho we are and the McGinnis story
Free The STR-Ops open dataset corpus Browse all
BreezewaySync turnover photos from the workflow you already run SlackFlagged turns pushed straight to your channel Chrome extensionOne-click photo upload from any open tab BoomConnect RapidEye to your Boom PMS turnovers
Works with your PMS and ops stack All integrations

Research Team

Rohan Nagabhirava

Co-founder, RapidEye · Data & Methodology Lead

Rohan Nagabhirava is a co-founder of RapidEye and leads data and methodology. He is a computer-vision researcher pursuing an MS in Artificial Intelligence Engineering at Carnegie Mellon University, previously worked on autonomy and perception at Rivian, and builds the technology that reads a property's condition from its photos.

MS, AI Engineering, Carnegie Mellon IEEE & arXiv published Named inventor, multiple US patents
LinkedIn Google Scholar Editorial process
Rohan Nagabhirava, Co-founder of RapidEye
Rohan Nagabhirava Co-founder · Data & Methodology

Background and expertise

What Rohan works on

Rohan co-founded RapidEye and leads its technical and data work. His field is computer vision: the science of getting machines to understand images, which is the entire foundation of RapidEye's product. Where most property software reads text, RapidEye reads pixels, comparing a property's photos across turnovers to flag new damage, missing items, and cleaning issues. Rohan builds that capability.

He is pursuing an MS in Artificial Intelligence Engineering (Electrical and Computer Engineering) at Carnegie Mellon University and holds a BS in Mechanical Engineering with a minor in Computer Science from the University of Illinois Urbana-Champaign. Before RapidEye he worked on computer vision and robotics automation at Rivian, with earlier engineering roles at Lucid Motors, General Motors, and Western Digital.

His published research spans hardware and modern vision. He is a co-author of "CAD-Prompted SAM3: Geometry-Conditioned Instance Segmentation for Industrial Objects" (arXiv, 2026), work on the exact class of problem RapidEye solves, and is published in IEEE Transactions on Magnetics. He is a named inventor on multiple issued US patents from his semiconductor R&D work.

Credentials

Education, research, and patents

Role in our research

How Rohan contributes

Every RapidEye report is produced by a named team with clear responsibilities. Rohan owns methodology: how data is collected, how each figure is verified against a primary source, and how disagreements between sources are handled (both ranges shown, never averaged into a false single number). His research training is what keeps the standard honest. Read more about how we work.

For questions about our research, data quality, or a specific report, contact the team. We typically respond within one to two business days. See the full RapidEye Research library.