Predict your Data
We don't just build state of the art machine learning models for your particular use case. We go from identifying possible ML use cases to implementing a learning and scoring pipeline. This enables you to retrain and score as often as you like, whenever you like. <a href='/dogs'>Example app</a>.
Segment your data
Customers, documents, images, etc. can often be clustered into sensible segments. This can be helpful e.g. to personalize offering, targeting or outbound communication. By combining cluster analysis with your domain knowledge we can arrive together on cluster interpretation / labeling.
Build NLP Pipelines
Encode your textual data in such a way that they can be plugged into any other feature listed here, such as clustering, lookalike detection, or variable prediction. Retrieve sentiment, classify inbound communication, or invent your own use case. <a href='/sentiment'>Example app</a>.
Analyse your data
Business decisions are often enough based on gut feeling or prejudices. Get it right by looking at te data. We perform thorough analysis and provide interactive dashboard visualizations, addressing the main and side questions you may have. <a href='/corona_outbreak'>Example analysis</a>.
Report your data
Do you create spreadsheets on a recurrent basis? Do you often perform the same repetitive steps in the process? We can automate the process from data to any kind of report you like, crunching data towards desired insights in a desired format.
Share data insight by means of lightweight, easy to use data applications, customized to your particular business needs. In other words, present your insights interactively. Whether it is to your team members or to your customers. <a href='/world'>Example app</a>.
You might have a particular customer, document, image or general datapoint, for which you want to find others that are similar. Maybe you want to expand your offer audience, or recommend similar content to users. Do so with lookalike detection.
Maybe you are looking for anomalies, data points that stand out from the bulk of your data by any given extent. Examples are fraudulent transactions, system malfunctioning, remarkable customers, and others. Trace them with anomaly detection.
Put it Together
Many of the features listed here can be put together. Imagine an integrated machine learning system, that takes in structured as well as textual data, and is evaluated / monitored by recurring dashboards. Finally, an application makes it available for prediction.