Artificial Intelligence in Real Estate
Real estate artificial intelligence is now a reality and can help you offer more data and better conditions to your clients.
The web alone can reveal a great deal about customer behavior, their propensities, tastes, schedules, and a huge number of other data that are put away on the net. With the development of innovation, the web, joined with different components, can further develop the shopping experience of an individual keen on some sort of property, for instance.
AI in real estate does the truly difficult work related to real estate transactions: the complex data, compliance, the desk work, the finding of the home, the arrangement, the offers. Basically, all that truly makes an exchange go a lot quicker, making it more straightforward and regularly less expensive.
Customer success stories
A workplace hygiene solutions company approached AI Superior with a unique task: to create a system capable of autonomously identifying when an area needed cleaning, reducing the need for
In today’s dynamic real estate market, accurately assessing the price of different zones within a city is essential for real estate professionals. However, this task has traditionally been challenging
AI Superior, in collaboration with an Ophthalmology Centre, has developed an advanced deep learning model to estimate the volume of fat and muscle in human eyes using CT and
AI Superior has developed an innovative solution for an insurance company that was seeking to provide usage-based insurance to their customers. Leveraging deep learning algorithms, AI Superior has created
AI Superior has designed an innovative solution for municipalities to rapidly detect and localise graffiti in their cities, using state-of-the-art deep learning algorithms. This real-time, high-accuracy graffiti detection system
AI Superior, in collaboration with international humanitarian NGO World Vision, undertook a compelling project to investigate the potential correlation between land degradation and conflicts in selected countries. By analysing
Our Project Approach
The AI project lifecycle has been adopted from an existing standard used in software development. Also, the approach takes into account the scientific challenges inherent in machine learning projects involving software development processes. The approach aims to ensure the quality of development. Each phase has its own goals and quality assurance criteria that must be met before the next stage can be initiated.
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