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Looks like they have the integration half of the equation. But also they need to plugin the summarization/synthesis utility to fully realize the value. There have been enterprise search apps in the past and they usually have failed because of A) too much data and no clear way to prioritize and B) keeping up with all the new systems that arise all the time.

If some kind of GPT pipeline could solve A above - and identify the relevant synthesis of data into a coherent answer it would be supremely useful. I usually can do a search in 3-5 systems manually - just getting the results while akward isn't the problem. The problem is knowing which nugget of info in 80 pages of slack search results is relevant to my problem.




prioritizing and extracting summeries from docs is what we do at gerev. https://github.com/gerevai/gerev to see for yourself.

Or you could try our sweet little demo: https://demo.gerev.ai




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