gIQ integrates LLMs potential and biologically inspired associative knowledge graphs into a coherent system that allows for fast and accurate identification of connections, correlations and patterns in data, enabling better decision-making.
Define and run LLM-driven workflows
Extract information from unstructured data
Categorize, summarize and classify with built-in and custom prompts
Prepare enriched datasets for further analysis
Define and run LLM-driven workflows
Extract information from unstructured data
Categorize, summarize and classify with built-in and custom prompts
Prepare enriched datasets for further analysis
Perform advanced analytics with graph AutoML tools
Make predictions, detect anomalies, identify clusters
Find frequent patterns and association rules
Explore data relationships reflected in knowledge graph structure
A global research and consulting company wants to enhance its automotive market research capabilities with Large Language Models (LLMs). By scanning a variety of data sources, the firm identifies emerging trends in automotive technology and compiles comprehensive quarterly reports.
gIQ serves as a well-suited solution to that need by enabling the firm to empower product planners and strategists with the confidence and clarity necessary for making informed decisions.
gIQ Workflows are instrumental in extracting pivotal information from blog posts, media articles, and other unstructured sources, focusing on leading technology trends within the automotive sector. This process of enrichment yields a detailed log of raw signals, indicating the involvement of automakers (OEMs) in specific technologies. This signals log is then processed through gIQ Studio, where graph visualization tools facilitate the identification and examination of technology roadmaps for each OEM. These roadmaps detail expected launch dates and provide an industry-wide overview of anticipated technology rollouts.
By combining Generative AI automation with advanced graph analytics, the company is able to not only expedite the report creation process, but also significantly enhance the precision of technology launch signal detection for both individual OEMs and the whole automotive industry.
A company specializing in motion control technology is looking to harness the power of data analytics to refine their testing procedures. As a global supplier of steering and driveline systems, as well as software for OEMs, they are facing challenges with the efficiency of backdrive testing stations in one of their production lines.
Their objective is to leverage data from preceding stations to dynamically fine-tune the operational parameters of backdrive machines, aiming to lower rejection rates and boost process efficiency. The ultimate goal is to design and implement an AI-driven solution that yields tangible enhancements in efficiency and accuracy, thereby reducing total costs.
gIQ played a pivotal role in pinpointing the critical gap in their strategy, which was the quality and sufficiency of the input data. It became evident that the data collected from the testing stations along the manufacturing line was inadequate for a thorough understanding of the entire process, including the configuration of prior steps and their influence on the backdrive machine settings.
Thanks to the graph associative algorithms integrated into gIQ Studio, the company was able to swiftly determine the impracticality of their initial plan. This insight helped them avoid the expensive and time-consuming process of trial and error in machine learning modeling and implementation with inadequate input data.
The US research division of a leading Japanese OEM faced significant challenges in accurately understanding and representing the complex and dynamic nature of the American used car market. Traditional analytical models and data structures were inadequate in capturing the intricate relationships and patterns that define the market.
The gIQ platform, with its advanced associative knowledge graphs capabilities, introduced an innovative approach to deciphering the used car market. It achieved this by mapping out the intricate relationships between vehicle specifications and broader market trends. Leveraging over one million vehicle listings from cars.com, along with additional datasets such as CO2 emissions and EV charger locations, the gIQ platform enabled a level of comprehensive analysis previously unattainable with conventional methods.
Employing the gIQ platform allowed the OEM to accurately predict used car prices by delineating the complex interconnections within the market. This capability was crucial in developing a configurable recommendation system. Utilizing the knowledge graphs, the system could provide personalized vehicle suggestions to potential buyers, markedly improving the user experience.
At the core of gIQ stands a team of dedicated, driven and passionate professionals. We bring a wealth of experience as visionary leaders, AI enthusiasts and seasoned entrepreneurs who have already steered multiple companies to success.
gIQ emerged from our collective vision, inspired by the challenges we faced in diverse customer engagements.
Drawing upon a rich tapestry of experiences, deep insights, and groundbreaking scientific research we have crafted a solution that addresses the real-world complexities of data analytics.
gIQ embodies our commitment to transforming challenges into opportunities for innovation and progress.
Hetmana Żółkiewskiego 17A
31-539 Kraków, Poland
g.IQ sp. z o.o. ul. Hetmana Żółkiewskiego 17A, 31-539 Kraków, entered in the register of entrepreneurs kept by the District Court for Kraków Śródmieście in Kraków XI Commercial Division of the National Court Register under KRS (National Court Register) number: 0001100802, NIP (VAT identification number): 6751796936; REGON: 528213218.
