Successful and satisfied customers are what drives us, innovative projects and products are our passion. Our achievements and references are based on close and trusted collaboration. Our customers also trust us with critical questions.
Our client - a large scale eCommerce shop - wanted to optimize the throughput of their logistics systems including autonomous, robotic subsystems. By analyzing network traffic and machine to machine communication within the warehouse Niologic successfully identified potentials for optimization and set up a predictive system for order routing.
Our client, a large manufacturer and distributor of outdoor equipment, wanted to establish a central platform for their data, as a basis for advanced analytics of customer data and business data. Niologic implemented a machine learning algorithm for pricing based on Google Cloud AutoML, dashboarding in Google Data Studio, and a central data warehouse using Google BigQuery.
Our client, a global management consultancy, wanted to acquire a data platform for the efficient use of data scientists in consulting teams. niologic implemented an analytics and data science platform for the consulting industry specialists.
Our customer, a large plant fire department in Germany, commissioned us with the evaluation of alternative drive forms for the accessibility of their site. niologic carried out simulations of trip time and accessibility with their software Premergency and compared several drive forms.
Our client, the product branch of a top-tier management consultancy, required a platform for calculating short- and long-term calculations. niologic planned and implemented a calculation platform based on the design principle of a serverless architecture.
For a company-wide multi-brand strategy, niologic implemented portfolio management as well as churn analyses and churn prediction. Using Bayesian probability and Kaplan-Meier, we implemented predictive analytics in an in-memory database. Furthermore, Rotational Churn for monthly subscription contracts could be evaluated.
To ensure further growth of a leading industry event, marketing and sales processes were to be digitalized and automated. niologic consolidated customer data and set up a CRM systems, as well as customer analyses in order to specifically address customers.
niologic created a generalized data structure for a data warehouse (DWH) based on Google BigQuery, Apache Airflow and the Data Vault 2.0 model. Through a high abstraction of ETL codes, a simplified maintainability could be reached for the DWH.
An analytic big data business solution was migrated from Amazon AWS to Google Cloud without any downtime. Afterwards, the foundation was laid for a cloud-native development, with the introduction of Kubernetes and container technology.
Improved supply chain management with data integration and process integration. niologic supported their customer by merging data into an installed base, business plans, order processes and a prognosis of demand.