vs Other Image Recognition Technologies Outshines the Competition in Real Estate Searches

The expression “Jack of all trades, Master of none” was never truer than for generic image recognition software. And, with consumers becoming savvy in their online searches, property websites need to show they can deliver relevant, quality images in less than a second. The only way online portals achieve this is through software built from the ground up to support the real estate sector, like’s plug-n-play product. categorizes, searches for, and delivers results on property-related images. Still, some real estate companies insist on losing time and money with generic approaches to image recognition. Google Vision*, Amazon Rekognition*, and Clarifai* cannot match the intelligence, accuracy, or speed of’s software solution. outmatches the generic solutions in three critical ways:

- image tags are real estate industry-specific;

- results are more accurate than those of generic platforms;

- response times are much faster than generic products.

A look at what others have noted about the three generic image recognition approaches and what delivers should help decision makers invest in the solution, instead of wasting time and money with the alternatives.

Remarkably Specific Tags 


*see disclaimer below’s computer vision technology can classify real estate property images into over 30 home scene categories and tag over 30 home features.’s image recognition technology identifies, tags, and describes properties the way real estate agents do. The artificial intelligence (A.I,) technology recognizes and organizes settings with references that real estate professionals use every day. The technology understands property-related contexts to a level of detail approaching that of many real estate professionals.

As good as Google’s*, Amazon’s*, and Clarifai’s* image recognition technologies are, they only see generic objects in photographs. They may decompose a photograph of a living room into basic and irrelevant components: a sofa; windows; chairs; a table; wood; a lamp. The generic platforms will even display results of non-real estate-related objects, like animals and people. Thus, the results produce clutter that frustrates customer searches. The likelihood is also great that generic products may not even recognize that the setting is a living room. will identify the context of the example image as a living room and tag objects relevant to a consumer’s property search. The software can display results that are specific and targeted to the needs of customers in the real estate market. So, individuals may see image tags like: beamed ceiling, fireplace, hardwood floor, and natural light.

Accuracy Rules

Customers rely on the accuracy of the query results websites display. If it is clear to someone searching for a property online that the service’s search algorithms are not delivering results that come close to what the client expects, they will abandon the site and go to a competitor’s.

The creators of's software built the product from the ground up to support the real estate industry. The accuracy of its search results reaches 99%. Meanwhile, generic image recognition software delivers accuracy rates in a real estate context of 78%. The difference in accuracy implies that generic products may produce up to 200,000 more “junk” results in a search of a million images than queries performed with cited in a report comparing Google Vision* and Amazon Rekognition* that 93.6% of Vision’s* tags turned out to be relevant, while only 89% of Rekognition’s* tags were relevant.

The marketing platform summarized in its own comparison of the three generic image recognition approaches that Clarifai* has the strongest technology for identifying generic contexts of images, Google* the best scene detection, and Amazon* the best facial analysis. 

With each of the generic image recognition applications sporting its own strengths, it is questionable whether any will ever reach the level of accuracy of's software when it comes to real estate.

Accuracy helps customers only to the extent they receive search results quickly.

The Need for Speed


*see disclaimer below’s specialized architecture and training on real estate-related images give it an incredible edge over its generic image recognition counterparts.’s own tests show the fastest rates for returns on end-to-end query results (image download + processing):

-> 0.5 seconds

-> Amazon Rekognition*: 1 second - 1.2 seconds

-> Google Vision*: 1.5 seconds

-> Clarifai*: 1.5 seconds

Many property website owners believe they can save a great deal of time and money by customizing generic image recognition software to meet the needs of the real estate industry. query results help images appear to customers in a split second, which suits user expectations from instant responses to online queries.

Still, some companies do not factor in the hidden costs of feeding and updating platforms, like Google Vision*, Clarifai* or Amazon Rekognition*, with the millions of images computer vision technology requires to be of use to website visitors. While generic makers Amazon* and Google* have brand recognition, their offerings are not custom-made for specific industries.

What matters to customers searching for properties online is results: relevancy to the real estate industry, an accuracy that reflects customer desires, and answers that are faster than the blink of an eye. offers all these facets and more.

* Average response times testing 10.000 images to each API.

* Results have been estimated or simulated using internal analysis or architecture simulation or modeling, and provided to you for informational purposes. Any differences in networking speed, server location, software, or configuration may affect your actual performance.

* Other names and brands may be claimed as the property of others. 

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