Hello r/CRE, have any of you ever used these machine learning lease reading and repository SaaS programs? Would love to hear feedback.
I’m a third-party commercial property manager and one of our clients request that we take a look at assess solution for storing leases, reading them using machine learning and creating lease summary/abstract. The service will also make everything searchable for keywords and able to run reports off of them (think Early Termination rights set to expire in 6 months). 

The product seems really promising, but wanted to hear thoughts if anyone has any.  Does anyone have experience with these?

Thanks!
[{"id": "fxdu3ti", "text": "What\u2019s the program called? I am intrigued. That being said, I am seeing a lot of these \u201cAI that can replace X job function\u201d from the sales person, but hoping on a call with techs in a demo it\u2019s not as great as it sounds.", "replies": [{"id": "fxdw811", "text": "\\&gt;\u201c\\[insert hot tech term\\] that can replace X job function\u201d from the sales person, but hoping on a call with techs in a demo it\u2019s not as great as it sounds\n\nThis is the tech industry in a nutshell (I work in tech)", "replies": [], "depth": 2}, {"id": "fxdxxl6", "text": "[Leaseable](https://www.leaseable.com) is the name of the product being considered", "replies": [{"id": "fxe1p2m", "text": "I thought about this concept recently.\n\nWould love AI for abstracting leases, mortgages docs, etc, but as others point out, it needs to approach 100% accuracy.", "replies": [{"id": "fxe3ed2", "text": "Mortgage docs (and other public records) might actually be the best use case for this.  I could see this being used to rapidly build a database for research or prospecting.  In either case you dont really need to be 100% and manually analyzing every mortgage doc in just one county would quickly be very difficult", "replies": [{"id": "fxfmadj", "text": "Here is a video of what the human-in-the-loop data extraction process looks like: [https://vimeo.com/427941992](https://vimeo.com/427941992)", "replies": [], "depth": 5}], "depth": 4}], "depth": 3}], "depth": 2}], "depth": 1}, {"id": "fxdxihn", "text": "Hey there! My company ([www.abstractcre.com](https://www.abstractcre.com)) does exactly that. Would be happy to give you a demo if interested! Feel free to DM me.", "replies": [{"id": "fxdy18d", "text": "Sent!", "replies": [], "depth": 2}], "depth": 1}, {"id": "fxdy95p", "text": "The time/cost savings vs penalty for errors seems really big on this.  A service to store leases and database abstract type data for reporting/analysis does seem useful but your machine learning is going to have to be 100% or missing a renewal deadline etc might cost you serious money.  Someone reading that manually and entering it doesnt take that long, it just seems unneeded to use ai for this.  Not that people cant make mistakes, but there is a lot of liability in this for whoever is making the software, or you as pm if you rely on it.", "replies": [{"id": "fxe0pb3", "text": "Exactly. 99% of leases might have a similar structure, but I have seen some really important and potentially very expensive language buried in unusual parts of the lease or using unconventional names for things that would make keywords very difficult.", "replies": [{"id": "fxe46oq", "text": "No doubt. We\u2019re working on an workflow for approving new leases, etc.", "replies": [], "depth": 3}], "depth": 2}, {"id": "fxfdoe5", "text": "We ([Abstract](https://www.abstractcre.com)) use a human-in-the-loop design so that users approve/deny every AI-generated prediction. While the data extraction is valuable, having data linked to the source of truth makes auditing easier and faster. Trying to organize this critical information in a web of file folders and spreadsheets is a huge compliance risk.", "replies": [], "depth": 2}], "depth": 1}]