behavior and risk prediction
from complex human interactions
using online machine learning
Request a demo


by industry


addressing specific data analytics needs


under the hood

Learns models online while the data flow so that the models are always up-to-date even when the character of the data changes.
Works with both streaming and batch data; can utilize stored big data before feeding directly from the source streams.
Our systems store data temporarily and can scale horizontally to handle large number of features as well as higher stream speeds.
Supervision Flexibility
Supports unsupervised, semi-supervised and supervised learning. When semi-supervised, your domain experts are presented only with a minimal set of data for labelling.
Human-System Interface
Allows rich and easy interactions with the user via responsive web interface.
Data Types
Works with heterogeneous types of data; text, numbers, time-series, networked-data, features extracted from video etc. all can be fed into out systems.
Optimized Complexity
Employs feature selection algorithms to allow models as simple as possible but not simpler.
Flexible Deployment
Deploys to the cloud as well as on local computing resources; deployment using Docker is possible.
Integrates with existing data streams, schema, and solutions; TCP connections, REST or Websocket connections, database interfaces and file systems are supported.


tazi team

Prof. Dr. Zehra Çataltepe


Dr. Tanju Çataltepe


Kerem Özçakıl

Customer Relations

Nadir Ulubey

Software Engineer

İsmail Kelebek

Software Engineer

Ahmet Çağlar Bayatlı

Software Engineer

Pelin Gümüşlü

ITU, Computer Engineering

Ozan Ata

ITU, Computer Engineering

Tarık Korkmaz

Koç University, Industrial Engineering / Computer Engineering is a machine learning startup.
The founders have years of machine-learning expertise in academia and industry and the design and operations experience with large scale real-time systems.

Contact Us

we are listening…