The company, based in the North East of England was established in 2005 to exploit and develop an enhanced version of a proof of concept software, a prototype for anomaly detection of a company’s financial data (specifically share price, volume of shares and number of shares traded), and assess the anomalies cause using a “Bag of Words” analysis of news from internet news sites.
Since the initial prototype, the company, now working with compliance officers for major banks, has refined the concept and developed a new design incorporating voice, text (from several e-comm systems), internet news feeds and financial trading data. The company’s continued interest in natural language processing of text data has led them to developing their activities towards other sectors which require data mining of massive textual data sets, typical of those found in marketing response and customer survey satisfaction studies.
The company has extensive experience of developing software for analysis of different data sources using open data languages such as Python on open data platforms such as Anaconda. It has also developed front and back end software using applications such as C#.Net and SQL Server and has developed expertise in the use of AWS (Amazon Web Services) as well as Google’s cloud-based data platform, BigQuery.
The company is now seeking partners in the following areas: Banks/hedge funds or other financial institutions who are willing to assist with development of the company’s anti-insider trading and market manipulation product offering. They also hope to attract IT companies with experience of front-end development and cloud-based architecture.
They are also pursuing companies to apply its expertise to firms dealing with large amounts of textual data. Typically, this could be in response to large scale marketing campaigns, customer response surveys on the feedback reviews of a new product, or the alteration to an existing product similar exercise of data gathering based on customer complaints against products or services in the hope of extracting common themes to issues raised by customers.