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Main areas of benefit.

Budget Savings ๐Ÿ’ฐ

We use open-source frameworks which enables us to onboard the latest AI trends quickly without a budget hit. Integration costs are also minimised.

Continuous improvement ๐Ÿ“ˆ

The system learns from your feedback and adapts its decisions accordingly => this enables you to detect new types of fraud quickly.

Customer satisfaction ๐Ÿ˜Ž

Our system can not only help with fraud detection, but also with better customer satisfaction.

Employee satisfaction ๐Ÿฅฐ

Our system helps you with routine works so your employees can focus on creative tasks. 24/7 service 100% covered by machine.

Our principles ๐Ÿšฉ


Open Source

Zero cost for 3rd party software.


Cloud Ready

No matter if you run on AWS, Google Cloud, Azure or on-premise, we are ready.



Prototyping can be done on local machine or small sandbox. Production can run on a huge cluster.



Enable only data sources you need. Detect only the types of fraud you want - a plug-in approach.

Performance ๐Ÿš€


  • More than 85%-95% precision depends on a case

  • 0.004% - 0.006% false positives ratio

Latency & Performance

  • Less than 3 minutes to classify 25 million calls

  • Approximately 2 minutes to prepare data set

Financial Benefits (T-Mobile CZ)

  • 1 FTE spared

  • 24/7 monitoring not needed now

  • 150k - 200k EUR yearly saved (OPEX)

Use cases ๐ŸŽฏ


  • PBX fraund - Hacked PBX, stolen mobile (SIM) identity, etc.

  • SIM boxing - Interconnection fraud

  • Spoofing - Detects fake call-centers, masquerading as a trusted entity

  • Wangiri - "one ring and cut" using premium numbers

  • Other features - payments fraud, flash-calls, gambling, etc.


  • Fraud detection in Telco network

  • Fraud detection on Google payments data

  • Data analytics platform on huge amount of e-com data (scraped htmls mainly + behavior data)

  • Customer segmentation for marketing and campaign optimization

  • Setting up environment + processes for data analytics (hadoop + spark clusters, backend for reporting systems etc.)

Tech. stack ๐Ÿšง

kubeflow icongitlab iconapache spark iconoracle iconhadoop iconkafka iconscala iconaws iconpython iconbitbucket iconkubernetes icontensorflow iconangular iconreact iconmysql icon

How we work ๐Ÿ‘จโ€๐Ÿ’ป



We analyse fraud cases specifics, data source available and decide on feasibility.


Proof of Concept

We prepare deployment of a real, representative scenario to evaluate performance and feasibility, this is cheap and fast.


Detailed design

We define how to integrate with the current systems/data sources and prepare infrastructure.



System is deployed, AI models trained and fraud detection is up and running.



System is activated and after certain monitoring period we ensure system is running OK considered in full duty.

Do not hessitate to
contact us. ๐Ÿ‘‹๐Ÿป

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