About Us
We are an agile machine learning and cloud data engineering boutique – a group of 20 experienced data scientists and data engineers with a combination of experience both from the agile world of digital start-ups as well as major international corporations.
We are supporting clients across Europe out of our Centre of Excellence in Prague – one of the leading European data science and technology talent hubs.
We are not selling boxes, we solve problems!
Generate value from data end-to-end
Tools are secondary – we will help you identify business opportunities, design how to best harness them and ensure that it happens from beginning to end. Especially in areas:
- customer analytics - cross-sell/up-sell, retention, CLV, segmentation, pricing
- digital marketing augmented by AI - microtargetting of ads, attribution, personalization, recommendations, online 360 unified customer view across channels
- fraud - fraud scoring, social network/graph analytics
- natural language processing - text classification, relevance matching, sentiment analysis, etc.
We help you build data products
Automated data solutions in production
We don’t end with nice slides and dashboards. The result that counts is an automated data solution in production integrated inside a real process. We are not report clickers but data scientists and data engineers. We have a strong focus on productionalizing data science solutions in the cloud with high standards of CI and automation.
We are your own data innovation strike team
Unique Data-Analytics-as-a-Service cooperation model
We don’t believe in outsourcing and delivering black-boxes. We will be your ”extended” team tightly integrated with your internal teams (or help you build them) to ensure you have full ownership of the results/know-how.
Agile and incremental approach
Experimenting, prototyping and agile
If you want to succeed double your failure rate!
Thomas Watson
No megalomaniac projects but fast prototyping and piloting of data use cases, failing fast and going from R&D to productionalization.
Modern cloud and open-source technologies, unique Spark and Databricks expertise and partnership
Focus on seeing and visualisation
No expensive proprietary solutions and large infrastructure teams needed – harness the cloud and open-source tools. Unique expertise and partnership with the Databricks Unified Analytics Platform for big data use cases.
Our speed and efficiency is enabled by use of modern open-source/light-weight and cloud based technologies:
- Open-source Spark, Python and R for data science
- Databricks as the data science+data engineering collaboration platform
- Keboola as the cloud data integration platform for BI/data science
- AWS, Azure and GCP for cloud infrastructure
- Qlik Sense, Tableau and PowerBI for visualisation
Key Areas of Expertise
Customer/marketing Analytics
- Segmentation, customer life-time value – who are my customers, which customers have the greatest value, on whom should I focus
- Churn and retention – which customers are at risk
- Cross-sell/up-sell, next-best action, campaign optimization – what to offer to which customers, when, campaign targeting, campaign sizing, personalized optimal offer, etc.
- Pricing and product analytics –how to set prices in order to maximize profits and minimize churn, price elasticity, cannibalization of products, seasonality, etc.
- Sales agents and channels – which agents and channels are the most efficient, which destroy value, which channel is appropriate for which customers and products
- Microsimulation planning
Digital/web/adtech Analytics
- Microtargetting of ads using machine learning
- Personalization and recommendations
- Measurement and optimization of digital campaigns across different channels, attribution
- Building online customer 360 view across channels
- Web behavior analysis, customer journey analysis
Cloud data engineering
- Our team of data engineers and cloud data architects works in two regimes:
- Productionalization of data science pipelines - as part of our data science projects
- Agile analytics platform - Builds a modern cloud data architecture for clients to support and enable use cases in the areas of data science, business intelligence and DWH
- an fully open solution based on standard AWS, Azure or GCP components
- Strong focus on data lake centric architectures combined with a agile DWH at the end of the pipeline
- Architectures combining both batch and real-time/streaming processes
Fraud
- Detection of suspicious events/transactions/clients/partners
- Support of investigation
Natural language processing
- Automatic identification of topics, categorization of text, etc.
- Sentiment analysis
- Predictive modelling using text data
