Extracting meaning from data
Finding and Extracting Data
Much of the data science work involves finding and extracting useful data. In order to do so, we use several approaches.
Firstly, we help our clients make the best use of their own data, by extracting and enriching the data from their own databases or ERP systems, such as mysql, postgres, SAP, etc.
Secondly, we collect data ourselves - from public APIs, through data scraping where appropriate, or by manipulating unstructured data into a format that can easily be digested - by humans and algorithms alike.
Data Cleaning and Preparation
The data we work often needs to be cleaned and prepared for further analysis.
In the case of structured data, which is mostly sourced from internal or external databases or APIs, most of the cleaning work relates to joining different data sources and treating cases of missing or mislabeled data.
In the case of unstructured or partially structured data, such as text, webpages, images, sound, the pre-processing can become a machine learning project in itself, involving sophisticated algorithms and tools to get the job done.
Data Analysis and Visualization
Once we have extracted and structured data (or during the extraction and structuring), we always ensure that we have a deep understanding of the data we work with.
This understanding often comes through a combination of creating aggregate results, that we either represent in the form of tables or graphs.
AI and Machine Learning
Making intelligent algorithms
A first group of problems we solve is related to making accurate predictions.
Making accurate predictions can help decision makers and managers make better, data driven decisions.
Thanks to AI, it is possible to discover hidden patterns that are unvisible or undiscoverable by humans. We have a track record in developing the right algorithm to solve particularly complex problems such as price prediction, production capacity, demand, ...
The prediction horizon can vary depending on the need and challenge that we are solving for our client: from hours to ... months and years.
The second group of problems we solve is related to optimizing existing businesses.
Many companies are willing to optimize their operations but struggle to manage several dimensions of a same problem at the same time.
This is where AI can help, by finding the best way to optimize a situation with several factors to be considered at the same time (season, weather, demand,...). We have a track record in solving problems such as setting prices, setting marketing actions, ...
The final group of problems we solve is related to quickly and automatically extracting useful information from videos and images.
Also called Computer Vision, or Machine Vision, we excel in solving problems such as object detection in million of images, parsing key information in thousands of documents, ...in real time.
APIs and integrations
Making our solutions easy to use
Programmatic Interfaces and dashboards
We believe great machine learning applications should be easy to use - whether it is by individual users or other applications. We build both user interfaces and API's - Application Programming Interfaces - to make sure our solutions are easy to integrate in any business.
Software as a Service
- Amazon EC2
We also develop machine learning solutions that serve a wider market need. We offer these solutions as a SaaS - software as a service. We take care both of the development and the infrastructure of the solutions, meaning our clients can scale up and down their use of the solution without incurring any significant fixed costs.
We build these scalable solutions using several cloud infrastructure providers, and technology that is made to scale, such as kubernetes and docker.
For clients who have compliance requirements that disallow them to use externally hosted applications, we provide ways of easily deploy our applications on their own networks.