We use a combination of embedding, aggregation and extrapolation
techniques to add value to the original raw data before connecting it to our proprietary
sources in order to grow the asset value. The significant enhancements are meant
to allow users to easy engineer different features when constructing models on the
We understand that every retail business is unique and thus the
tools for data science have to cater for that. Our algorithms and models are transparent
to users in order to allow them to tweak the parameters to match the business objectives.
Users are also encouraged to ask our helpline or read the internal wiki should they
have any questions about what is best for their business.
There are too many research papers in the artificial intelligence
realm. We comb through them to study relevant research materials and open source
projects so that users don’t have to. In order to boost the effectiveness of the
platform, we are constantly engineering new possibilities behind the scenes to create
a better data science experience.
How do we do it?
Our take on solving your merchant dashboard's problems.