Smarter.Codes

Problem

Our client is a real estate agency with 100+ agents. For every deal, they must fill out 10+ lengthy forms in their back-office software. This was time-consuming for busy agents. And even impossible for newbie agents. Many would abandon forms in the middle, or would fill them incorrectly or slowly! The aim is to increase the number of forms that are filled in, correctly.

Solution

On one side we would keep open the back office software, and on another side, we would keep open our chatbot. Using plain English, our chatbot would prompt the user with questions, & automatically fill the forms based on answers. Users could observe forms getting filled out on their screens. The chatbot would navigate across the sophisticated sequence of steps – by navigating between screens of software – without the user having to leave our chatbot. On a few certain confusing fields, the chatbot would also advise users on what to fill in those fields.

Result

In 8 weeks, the team at SmarterCodes built an RPA + Chatbot, which our real estate agency client began to beta test across their team.

Approach

This RPA was built differently. Typical RPAs use the “selector” technique to identify GUI elements and operate on them. Ours rather employed Computer Vision to understand the GUI elements. This resulted in a better developer experience because some of the GUI elements in back office software weren’t operable with the selector technique. But with Computer Vision we were able to operate just like a human agent would.

To interpret the language of the user, we used a mix of Google Dialogflow and our own proprietary NLP AI. Google Dialogflow’s ready-made AI helped us prototype faster, and our proprietary NLP AI helped us interpret some curve-ball natural language inputs where

Problem

Our client offers market research to its customers. Helping customers identify the demand & supply of certain businesses in a given region. With their database offering location data of 1 Billion+ businesses, they wanted to expand the records in their database.

Solution

We built an Internet crawler. Much like a typical Search Engine crawler – it would go to millions of websites every day and employ AI and NLP to identify the office locations where their business operates from. These office locations would be extracted from special web pages of each website. For pages like Contact Us, About Us, Office Locations, etc

Result

Our client was able to expand their database of 1 Billion business locations to 1.5 Billion business locations in a matter of 6 months. This 50% increase was attributed to the Location Extract Internet Crawler we built for them.

Approach

A lot of websites today are built using rich internet applications. Because these websites make heavy use of Javascript, the typical HTTP crawlers are not able to interpret the content in such websites. To solve this we built a Deep Web Crawler. Our crawler would navigate the websites with javascript enabled web browser,
and navigate through a network of links on the website much as humans would. To speed up this crawler & save on crawling costs we also implemented Focused Crawling. This enabled our crawler to prevent going from the entire website content, but rather use AI to identify just the key pages where location data was most likely to be found. By identifying which web pages for Contact Us, About Us, and Office Locations, we would navigate directly to them. To access only the information we were interested in. Finally, we used a combination of Natural Language processes and Computer Vision to identify the very region of the pages where Location Details were mentioned.

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