Why Choose Us
We use data to revolutionise your business. We set ourselves apart with our multi-disciplinary approach, getting to the root of the business problem before delivering the most suitable solution in order to improve our clients' businesses and maximise ROI in turn.
Scope & Explore
1. Get an understanding of your data along with the key business challenges you are facing.
2. Scope out potential Al initiatives and prioritise based on what is a priority for your business.
3. Experiment and explore with your data, building some initial models to test potential.
Prototype & Develop
1. Build prototypes to test initial ideas and hypotheses, optimizing for performance along the way.
2. Run Pilots on real-life scenarios to get initial proof of value.
3. Learn and iterate based on results from initial pilots.
Deploy & Scale
1. Deploy and scale successful pilots into a production environment.
2. Continue to iterate and improve over time in order to increase the benefit.
3. Measure and track success.
Manage and deliver end-to-end Data Science and AI projects using our international network of the best Data Scientists, Engineers and academics.
Create Machine Learning and AI solutions to answer key business questions and deliver tracable business benefit. Full implementation from idea generation to deployment to production.
Create dynamic, user-friendly data dashboards to visualize our clients’ data. Unlocking new business insights to enable more informed, strategic and operational decision making.
Create and automate your key data cleaning and transformation processes.
With experience working at Capgemini Consulting, Daniel continues to exceed clients’ expectations with AI and Machine Learning. He specialises in connecting the commericial ‘so what’ to the delivery of state of the art AI to solve critical business challenges.
Having previously worked at Facebook and Yandex as a Machine Learning Manager. Fedor is experienced at delivering complex, multi-faceted AI projects end-to-end. He specialises in building computer vision, NLP, and scalable recommender systems.
Kunhee has a PhD in Mechanical Engineering from UCL and a track record of solving complex, multi-disciplinary problems. His area of expertise is computation fluid dynamics in which he has built AI based solutions to deliver new insights.
Lack of a central, joined up understanding of how to treat different customer groups differently in terms of marketing and sales activities.
B2B customer segmentation using a combination of secondary market data from Euromonitor and financial data from past customer orders. Customers were first broken into their constituent sectors and then further into product category in terms of customer needs. The final step was to cluster customers by buying metrics and patterns, such as order frequency, recency, size and profitability. Clustering algorithms were applied to segment the different customers.
In a music streaming world, there are now vast amounts of music metadata and of course streaming data at a user level. A new product that music labels having taking advantage of is curated playlists, at the time there were over 4000 playlists managed by the client. The management of all these playlists was manual and labour intensive using excel to track and maintain playlists.
Using the streaming platform APIs, web apps were constructed to allow syndication of playlists directly, allowing playlist management to be centralised and standardised. The playlist track order was optimised by applying a bespoke algorithm to filter the time series data. The final part was a recommendation engine used to recommend new tracks for a given playlist. These three parts in tandem allowed for a close to full automated solution for playlist management.
Due to a number of organisational restructures over the past few years, there was no Data Science team in place and overall limited understanding of how said function would integrate with the organisation’s overall strategic vision and operational set up.
Assisted in defining the organization’s target Data Science Operating Model and best practice, workshopping potential Data Use Cases with the wider business and prioritising them using our prioritisation framework.
The sales team had a lack of understanding of customer behaviours and attributes and ultimately how they should be treating discrete customer groups differently.
Clustering algorithms were deployed in order to group customers together based on similarity in terms of financial and core behavioural metrics. Ultimately allowing sales and marketing to make more informed, objective decisions.