Hiver vs. Hugging Face

One scenario where Hiver works really well is in a shared support or billing inbox or anywhere multiple team members need access to the same messages. For example, when a customer emails about a billing question, we used to reply and manually loop in the billing team or forward the message. That often caused confusion, especially when someone forgot to “reply all” or responded only to the teammate instead of the customer.Now with Hiver, we just assign the conversation to the billing inbox, and whoever is available can respond. Everyone on the team can see the thread, status, and notes, which avoids duplicated efforts and missed replies. We also used to rely on Google Groups, but it wasn’t clear if someone had already replied, and there was no easy way to collaborate behind the scenes. Hiver completely solved that for us.As for situations where Hiver might be less ideal, I’d say if your team isn’t using Gmail or Google Workspace, then it’s probably not a good fit. It’s really designed for teams that live in Gmail so the team doesn't have to learn a new system. I'm sure some businesses will require a more robust ticketing solution but for our use case, this was a perfect option to improve outcomes, client service, and satisfaction.

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If an organisation has more access to data and have access to high end computers like GPUs it’s recommended to use Hugging face as it will give better accuracy than any other models. If an organisation having less data and has less access to GPUsis looking for decent performance then traditional algorithms are more appropriate than hugging face

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• automation through rules
• tagging emails, anaylics behind them
• support, chat always responds right away

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• Model APIs
• Hugging Face Spaces for deploying demo apps
• Latest updated models available easily
• Vast support for language parsing and other relevant tasks

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• It could be considered expensive, but ultimately worth every penny for those who use shared inboxes/groups to run their business.

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• Most of the Hugging face models are of big size, hence difficult to work if there is no access to high computational system like GPU.
• It’s good to have some visualization tool in hugging face for viewing model architecture.
• I recommend to implement hugging face lite version so that it can run on any system with less specifications.

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Easy to use

Support is there, can be slow at times.

True ticketing solutions are certainly more robust in that you can typically manage social media channels and phones, etc. But, in my opinion, when you don't have a team solely responsible for managing a ticketing system they can be complicated to implement. With HIVER, we're already all accustomed to managing our own G-Suite inbox, so it was simply a matter of teaching the team how to utilize the solution.

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There are some other services offer similar capacity as to Hugging Face, but not entirely the same. For example, amazon web services have a machine learning service called Comprehend, which offer a set of easy to use APIs to do machine translation and entity recognition and some other common NLP use case.

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• The ROI on showing how many emails are being received and addressed in a timely manner are great for KPIs
• the ability to search and ensure emails are not being deleted as an automatic report is sent out the following day saying if any emails were deleted by whom
• the ability to automate who gets assigned what emails from specific vendors per entity

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• Hugging Face is cost and time saving.
• Pay is less, you pay what you use, doesn't affect much.
• Overall positive impact on business.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info