Comment by crawdog | Hacker News Reader

Comment by crawdog | Hacker News Reader

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Interesting to see more players joining the market. You can't walk into a large Enterprise and start your search conversation with "Your developers just build ____". Otherwise customer will want to build it themselves.

The killer feature I haven't seen with many of these solutions is easy, out of the box integration with internal systems (Atlassian Confluence, JIRA, Remedy, SharePoint, FileSystem, Intranet). When you have a SaaS search engine it's difficult to export that data... Even worse to secure it. Ironically, Plumtree Software (bought by BEA -> Oracle) had all of this in their product in 2001. What's old is new again... Those features are prime for a comeback.

I think this is a space where Elastic can do well with an on-prem or managed cloud offering that is "behind the firewall", integrated with customer's environment. Add in term vector search support, ML for document/query understanding, and integration with customer's security model (Active Directory) and it would be compelling.

You will need very powerful hardware to deploy the deep learning models on-prem for incremental learning.

And most of the time, while not indexing, the hardware would be sitting there sleeping. Probably not very cost-effective for enterprises.

> the hardware would be sitting there sleeping. Probably not very cost-effective for enterprises.

Not to be condescending, but idle hardware isn't even on the radar as far as waste goes in enterprises. An on-prem solution that is idle for 364 days of the year is completely fine for most of these companies.

For the ones that do care, that's what they make virtual machines and over-subscription for if they even care the slightest about that.

Also - see the rise in popularity of OpenShift/PCS/PKS - flexible infrastructure is also catching on.

You will need very powerful hardware to deploy the deep learning models on-prem for incremental learning.

This isn't true.

I've build (neural-network) vector based search extensions for search. You don't train the model - you use a pretrained model (that understands English in your domain) and then use it as an encoder.

Sometimes there is once-off pretraining process for domain adaptation, but honestly this isn't a big deal. Even on a CPU based machine you could do this overnight or over a weekend, and since it is once off that time doesn't really matter.

For large (mature) enterprises, I believe at this point it's safe to expect some level of hybrid cloud architecture. I also agree it would be very difficult/impossible to support this for "realtime" indexing.

lucidworks or sinequa are already doing that in the enterprise search space

Coveo is doing it better

You could throw ThoughtSpot in the mix as well. Interesting to see all the search engines position in the "Insight Engine" space now. Mix of analytics/search, but still very customer specific/custom. Curious how many are successful out of the box.

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