For 350+ years no one has attempted to build a Probability Machine, so we set on building the 1st one ever. Because probability is ubiquitous and foundational to many statistical and machine learning tasks, we believe that the Flek Machine will be a landmark in the AI field.

Flek is a unified framework for AI Analytics. It is a foundational development library that includes 3 main components: FlekML, Flek Server and Python Toolkit. ​ Essentially, Flek allows AI citizens to build probabilistic models, develop ML-driven applications as well as run both exploratory and predictive analytics – all in one integrated platform.

A Probability Machine is a special kind of machine learning engine that learns, stores and serves Nuggets (probability like objects). It learns these Nuggets from semi-structured data. It then allows users to query and mine the Nugget store to search for probabilistic patterns or to perform complex probabilistic computations. The probability machine can also serve these Nuggets and make them available for prediction or classification purposes.

For organizations, Flek is geared towards:

SME (small to medium enterprises) that need to apply ML and cannot afford large data science teams.

Larger enterprises that need a fully integrated platform to help them answer varied AI questions – without drowning in a swamp of complex models and pipelines that are very difficult to maintain and share among different users and use cases.​

For end users, Flek is intended to serve the needs of a mix of AI citizens: data scientists, programmers, statisticians and business analysts.

Thanks to its varied capabilities, Flek can meet the demands of a wide range of core sectors and business applications, including in:

- Transport
- Logistics
- Energy & Industry
- Knowledge & Intelligence

Both Flek and RDBMS have a core engine inside that serves user requests. In case of RDBMS, users can send SQL queries to retrieve a single record or multiple records joined from from one or more tables. In Flek, users can fetch a single Nugget (probability like object) or search the model store using a filter. They can also run auto-discovery algorithms that search for patterns, associations, rules, anomalies or causal relationships.