Statista_
Executive Summary
AI prototype for statistics portal
The statistics portal Statista is developing a new AI-based data research service that combines the vast capabilities of modern LLMs with the exclusive facts from the Statista data pool. The goal of the collaboration between AI experts from the TalentFormation Network and Statista's in-house team was to increase the speed of data retrieval and response generation, improve response quality, and significantly reduce operational costs.
The project team's innovative, data-driven approach led to a comprehensive redesign of the prototype using state-of-the-art LLM technologies. Through customization and over 100 targeted experiments, the performance of the system was significantly improved. The impressive results of this project - a significant reduction in response times while reducing costs and improving quality - demonstrate the impact of external expertise and innovative collaboration.
PERFORMED
experiments
100
reduced
latency time
10%
Decreased
operating costs
65%
improved
data quality
140%
The Starting Point
Statistics portal uses AI-based service to improve customer experience
Statista, one of the world's leading statistics portals, provides users with access to an extensive database of statistics, forecasts and infographics. To further improve the user experience, Statista developed a prototype for AI-powered research that needed to be optimized in terms of data quality, speed, and cost. With rapid developments in the field of artificial intelligence and limited internal resources, Statista sought a solution to make the prototype more efficient and perform better.
IN THEIR WORDS
Dr. Ingo Schellhammer
Chief Technology Officer
The Goal
Faster and more accurate search results
In collaboration with TalentFormation, Statista set out to improve the performance of the prototype to provide users with relevant statistics for their specific questions quickly and accurately. The focus was on accelerating data retrieval, improving response quality and speed, and significantly reducing operating costs to increase competitiveness. The project also aimed to build internal AI expertise.
Project goals:
- Improved data quality
- Increased speed
- Reduce costs
Ai for your Business_
Our Approach
Collaborate effectively with external experts and internal teams
Heureka Labs' experienced AI experts from the TalentFormation Network used a data-driven and experimental approach to define clear performance indicators for quality, speed and cost. Through close collaboration between the TalentFormation team and Statista's internal development team, we were able to implement significant measures within four weeks:
The Outcome
Impressive results in record time
The results speak for themselves: in just 4 weeks of the project, we achieved an average reduction in response times of 10%, with peaks of up to 65%. At the same time, operating costs were reduced by an impressive 65% and response quality increased by 140%. These results demonstrate not only the effectiveness of our approach, but also the potential that lies in combining expert knowledge and collaborative working.
The TalentFormation Factor
This project highlights the value of external expertise and a collaborative mindset. The experience of the experts from the TalentFormation network not only led to significant improvements in the prototype, but also laid the foundation for the further development of Statista's internal AI capabilities and resulting technological competitiveness.