The problem Tenouse solves

Technology has immensely evolved in the last two decades. The major fields where technology has reached the next level were space technology, food delivery and restaurants, travel, finance, clothing, job boards, social media, etc. But one of the most important fields among the three necessities of Life (Food, Shelter, Clothing) that was missed out is Shelter. One of the typical problems most of the people face is the problem of intimation of houses; be it that the houses they want to sell don’t get a maximum reach without the intervention of the third person or they can’t find the property of their dreams despite there are so many good properties longing to be known. Tenouse, which is an Artificial Intelligence based agile intimation of houses for tenants, landlords and other folks, significantly solves this problem of house reach, finding the right customers, eliminating third-person intervention, owner/tenant deposit/rent insecurity, etc, by its intelligent features which are used for classification, verification, search and match, gamification, smart prediction, visualization, which are powered by Machine Learning, Natural Language Processing and Data Analysis, and is a breakthrough point for the technology to marvelously evolve and excel in this field.

Challenges we ran into

The plan execution and project implementation was successful, but there is no doubt great innovations face great challenges. Our biggest challenge was how would we implement a system that would create a breakthrough face in the technological field, since there are three necessities of life (Food, Shelter and Clothing), and there is no advanced technology in Shelter(House) but Food and Clothing have next level technologies. So the biggest hurdle we faced was in the beginning of the project about how we could propose a system that would alienate all the similar application in the market today and sustain this application and take the technology in this field to the next level. There were also version issues in the code, since we (teammates) were working on different versions of the technology and that had created a mess in the project. But patiently we could fix all the issues and we could successfully implement the proposed system.