Prototyping is the conversation you have with your Idea.

The prototypes we made


How this works: When a user sends an email to the paralegal, the email server downloads the attachments from the incoming email and feeds those attachment to an AI application (of the organization he works), which returns the concepts of the attachments as the result then once it gets the results, it replies to the user by including the results as the email’s body.

Message Mate

How this works: When a user sends an email to the message mate, it  reads the email and sends it to one of the AI(s) (of the organization he  works) which disambiguates the emails and
once it gets the  disambiguated results, it reaches back to the user who sent the email with the results as the  email’s body which tells you what is the email about.


How this works:  When a user forwards an email he/she received to Lookup it  takes out the original sender’s address (who sent it to the “Lookup” user who  forwdwarded the email to “Lookup” ) from a forwarded email and

browses  to the domain taken out from the sender’s email address and scrapes  contents of the web page found at the domain  and disambiguates them  using one of the AIs (of the organization he works) and replies to the  user with the concepts which can tell the user about the original sender.

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How this works:  When a user email to neo with the Subject “Risk Disclosure”  with an attached document, neo takes the attached document and feeds  it to the AI application (of the organization he works) which analyzes the  input document and calculates the risk factors, and then neo replies to the  user with the risk factor information in HTML format, once it gets the risk  factors.

Indexer (Front-end)

When a user Input keywords to search which documents contain those keywords using one of the Urvin’s AIs and displays the results of the document name,  the block-type and searched the phrase.

Text Analyzer (Front-end)

That was the first prototype we built that use Urvin’s Disambiguation AIs to analyze the input text to get the concepts from the analyzed text.

Morgan (Front-end)

A user uploads a pdf file as the input and choose an extraction Type out of the 3 types (Days of non payment – equipment lease dataset, Notice entity – equipment lease dataset,Enforcement action summary – SEC dataset) and will send to one of the Urvin’s AIs which extracts contents and returns with the Results based on the extraction type the user selected.

Other Analyzers (Front-end)

With using the Urvin’s analyzer AIs we’ve made a lot of other analyzer prototypes.Dex,Matcher,NDA Analyzers,MovieSearch, Ngrams.

Rabbit Hole (Front-end)

Rabbit Hole is an AI teaching assistant by Urvin.AI that designed to answer student’s questions on the assigned reading material as well as guide them through assignments by linking concepts throughout the material.
A powerful and versatile prototype that can help you to monitor and manage your Wi-Fi and Bluetooth devices with ease. Whether you’re looking to optimize your network performance, troubleshoot issues, or detect security threats, it has everything you need to get the job done.
Since, cryptocurrencies have become increasingly popular in recent years, with more and more people using them for a wide range of purposes, we have built a marvelous prototype that unlocks the mystery behind the use of cryptocurrencies. Whether you are a cryptocurrency enthusiast or just curious about this exciting new technology, our prototype will become the perfect tool for you!

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