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Real Estate Sales Recommendation Platform

A first-of-its-kind educational resource and proprietary recommendation engine for homeowners researching and evaluating the many ways to sell a home.

The recommendation engine is powered by machine learning that considers thousands of data points for individual properties, local markets and individual seller preferences. Users are asked a few simple questions about their home and their top objectives during the sales process. Proprietary technology then evaluates the top local real estate agents, institutional buyers, discount agent models and more to match the seller with the option that will best help them achieve their goals. The service also provides a personal concierge to assist the homeowner through their selling experience.

Provided Services:

  • Back-End development, DevOps setup
  • Development of a quiz, calculating the approximate cost of a house, compiling a list of the 5 best realtors, displaying the nearest recently sold houses on a map and statistics
  • Implementation of a dashboard for sales department with agent statistics
  • Implementation of the client’s idea of selling 2 places from the list to agents
  • Referral system development
  • Server setup and optimization


  • Received Inman Innovator Awards in 2019
  • After we implemented the idea of ​​selling slots in Zip – in the first month, it delivered company $50k.
  • We are currently working to reduce costs on AWS: In September, they paid $8400. Forecast for November – $7000. And we also plan to reduce it to $6600 this week
  • Implemented a tool for salespeople to help identify agents. The service “guaranteed display” will be useful to them.


  • It was necessary to use MongoDB with setup backups, scaling and managing
  • The platform had limited calls to Salesforce per minute, so there was a need to increase it
  • The client wished to be able to view analytic reports
  • Needed to reach stable server working in a period of high customers activity (Tom Ferry summit etc.)


  • We decided to choose MongoDB Atlas cloud database service, which covers the mentioned demands
  • We chose Amazon SQS for the purpose to stretch in time all calls to Salesforce from all microservices.
  • Used Stitch to setup ingesting data every 30 minutes to Redshift database from MongoDB, Salesforce and PostgreSQL. Then we used Looker to create charts from Redshift tables.
  • Made a decision to set-up autoscaling instances in case if CPU utilization is over 80% during 3 minutes