In the UK search is a one-man show, with Google hoovering up around 90% of the market while Bing and Yahoo battle it out for the rest.
So it takes a brave man to start a company that aims to take on Eric Schmidt and co at their own game.
But as co-founder and CMO of WeSEE, Adrian Moxley has risen to the challenge. His startup has created a visual search engine by using image recognition technology to classify pictures and photos across the social web.
I spoke to Moxley to find out more...
In one sentence, what is WeSEE?
WeSEE is a web data classification company, which uses patented pattern recognition technology and semantic understanding to classify all types of image and video content for the purpose of online advertising, content verification and search.
What problems does WeSEE solve?
The web has evolved. Nowadays, users can upload their own content and distribute it, which brings fresh challenges as Internet companies can no longer manually control and classify their content, particularly social, visual and video content.
This is where WeSEE steps in – we make visual content brand safe and targetable for advertisers and searchable for publishers and consumers.
When and why did you launch WeSEE search?
WeSEE search launched in beta in November 2012 to help consumers of social media find visual content on social networks.
Using a special algorithm that understands and identifies all types of visual content, WeSEE discovers social images that are shared publicly even if the users who originally shared them never created a description for them.
WeSEE Search connects users and those who share social content together.
How are you funding the company?
We are currently funded through a mixture of private and institutional investors.
Who is your target audience?
For our search product our target audience are web users who are interested in the rich social content shared by their peers. For our ads product it’s any business found within the online advertising ecosystem - social network publishers, agencies, exchanges, DSPs and SSPs.
What are your immediate goals?
Our immediate goals are to increase the number of web resources in our index and improve our granularity when identifying visual images.
What were the biggest challenges involved in building WeSEE?
The biggest challenge was when to launch the technology. Building the technology and indexing took time, but wasn’t the hardest part.
We knew we had great technology, but figuring out how to adapt it and connect to third-party customers was the most difficult.
How will the company make money?
WeSEE will make money through offering brand, advertisers and agencies the chance to place relevant, targeted ads based on images that a consumer is viewing. We also believe our search functionality lends itself to licensing, particularly within ecommerce.
Who is in the team and what does it look like?
We have a fantastic technical team that comprises of top-level mathematicians, architects and even a rocket scientist.
On the commercial side we have a strong team who have worked together previously and who have a proven record in monetising technology.
Where would you like to be in one, three and five year’s time?
We would like the word WeSEE to be synonymous with visual content classification in both static and moving images.
We’d like to be the ‘Intel inside’ of automated image classification – if you use WeSEE technology it means the tech is fast, accurate and reliable.