Case Studies

Real results from our data projects for Czech companies

60-80

MD effort

70%

Time savings

Successful projects

100%

Client satisfaction

Technology

~25–35 MD

Data Distribution via File Storage

Challenge:

Inefficient data sharing with internal and external partners, high maintenance costs and slow data access.

Solution:

Implementation of standardized data sharing mechanism using dlt for source connections, DuckDB for deduplication and aggregation, output to partitioned Parquet files in cloud storage.

Customer Benefits:

Standardized and efficient data sharing mechanism for internal and external partners
Cost-effective data storage and delivery via cloud object stores
High performance for downstream analytics (Parquet format, partitioned)
Reduced maintenance overhead thanks to simple and reliable architecture

Technologies Used:

dlt DuckDB Parquet S3 Azure Blob Storage Google Cloud Storage Cloudflare R2

Commerce

~35–45 MD

Customer Category Prediction

Challenge:

Inefficient customer segmentation leading to poor marketing personalization and sales targeting.

Solution:

Implementation of predictive model using CRM and accounting system data, transformation with Pandas, machine learning with Scikit-learn and visualization in Power BI.

Customer Benefits:

Predictive segmentation improving marketing personalization and sales targeting
Increased campaign ROI through better customer category understanding
Reduction in churn by identifying at-risk customers early
Integration with existing CRM for automated category updates

Technologies Used:

CRM system Accounting system Pandas Scikit-learn Power BI

Manufacturing

~30–40 MD

Market Price Monitoring

Challenge:

Insufficient overview of competitor pricing and market trends leading to suboptimal pricing strategies.

Solution:

Continuous monitoring using Crawlee web crawler in Docker, data transformation with Polars, visualization through Seaborn and Power BI.

Customer Benefits:

Continuous monitoring of market and competitor pricing
Fast identification of price trends and outliers
Data-driven pricing strategy increasing competitiveness and margins
Automated dashboards enabling marketing and sales teams to react quickly

Technologies Used:

Crawlee Docker Polars Seaborn Power BI

E-commerce

~20–30 MD

Partner Data Monitoring

Challenge:

Manual data downloads from partner systems and inconsistent data quality for partner performance tracking.

Solution:

Automated data collection using Apify, transformation in DuckDB for data cleaning and aggregation, storage in Microsoft SQL Server.

Customer Benefits:

Automated data collection from partner systems replacing manual downloads
Improved data quality and consistency for partner performance tracking
Real-time alerts for anomalies and missing data
Reduced operational workload for data management teams

Technologies Used:

Apify DuckDB Microsoft SQL Server

Technology

~60–80 MD

Business Intelligence in the Cloud

Challenge:

Inconsistent reporting across business systems (sales, finance, CRM) and manual report preparation consuming 70% of time.

Solution:

Implementation of cloud BI solution with dlt for data ingestion, Google BigQuery as data warehouse (3-layer architecture), Dataform for orchestration and Looker for visualization.

Customer Benefits:

Unified and automated reporting across all business systems (sales, finance, CRM)
Significant reduction in manual report preparation time (−70%)
Improved decision-making through near real-time insights
Scalable cloud architecture reducing long-term infrastructure costs

Technologies Used:

SAP HubSpot Microsoft SQL Server dlt Google BigQuery Dataform Looker Google Colab

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