AI Product Data Mapping [Development Notes]

Companies that are steadily developing and expanding inevitably sooner or later face a situation where there is too much product data and its quality is deteriorating. These problems lead to the fact that your products take longer to reach various trading platforms, while your customers don’t receive fully correct information about products and services. This directly affects sales, audience loyalty, and brand image.

Gepard PIM platform has already harnessed the power of AI-driven solutions to automate and streamline data mapping — a complex and critical process in product content syndication and enrichment.

This Bintime Tech Talk delves into the intricacies of Gepard’s AI Mapping development, exploring its technology stack, challenges, and transformative benefits for eCommerce businesses.

What was the AI Mapping Development Tech Stack?

Gepard’s technology stack primarily revolves around OpenAI, with a specific focus on the ADA model for text-to-vector transformation. OpenAI plays a main role in automating the mapping process between various attributes, categories, and attribute values.

“For instance, when a customer provides their unique product data, OpenAI matches it seamlessly with marketplace requirements. This automation significantly reduces the need for manual interventions and speeds up the mapping process.”

Why was the ADA model chosen over other OpenAI embedding models?

When Gepard embarked on this AI-driven initiative, the ADA model was the only embedding model available from OpenAI. As OpenAI expanded its offerings, ADA continued to be our choice due to its proven effectiveness in translating text into meaningful embeddings (vectors). These vectors enable accurate comparison and matching of textual data, making ADA invaluable for our mapping needs.

Challenges of Creating AI Data Mappings

Initially, our developers considered using prompts as a mechanism for data comparison within the OpenAI framework. They thought that they would initially add some specific prompt for comparing the data — and ask OpenAI to analyze whether these values are similar to each other. However, this approach proved to be both costly and less effective in comparison to using embeddings.

Handling of complex data structures

“In fact, the product can map, for example, 2 complex taxonomies with hierarchical nesting, which have 20-30 thousand entries.”

Even though OpenAI facilitates only 1:1 mappings, the Gepard platform manages more complex mappings involving millions of products, thousands of categories, and tens of thousands of attributes.

Tips to optimize the performance of AI mappings

In optimizing the performance of AI-driven data mapping, the Bintime team prioritizes the sequence of operations to ensure efficient and accurate results. Vector databases play a crucial role in this optimization, as they are essential for working with OpenAI’s embeddings.

While we can’t enhance the model’s intrinsic accuracy, the focus remains on refining the mapping process for maximum efficiency and reliability.

How do you manage the costs associated with using OpenAI for mapping over 120 million products monthly?

Despite the costs associated with OpenAI’s services, Gepard’s AI mapping solution offers substantial cost savings compared to manual processes. By utilizing AI, Gepard reduces costs by approximately 70%, making the investment in OpenAI’s technology financially viable.

“On average, Bintime team expenditure on OpenAI amounts to around $5 per year, highlighting the cost-effectiveness of AI-driven solutions at scale.”

How does the AI mapping adapt to catalog & data changes over time?

Gepard AI-driven mapping relies on embeddings, which consistently return the same representations for identical phrases or data points. This consistency ensures that the system remains adaptable to changes in client data or data structures.

“Even if there are alterations in product names or catalog structures, the system’s reliance on embeddings ensures that these changes are not detrimental to the mapping process.”

How do you manage error correction in AI Mappings?

Understanding the potential for inaccuracies in data comparison, Bintime employs a proactive approach to error correction. The system considers multiple data-matching options and presents users with the most relevant and accurate data choices.

“By anticipating potential errors and providing users with multiple relevant options, we minimize the likelihood of incorrect or inaccurate data mappings.”

Future Tech Trends

Bintime is exploring the possibility of leveraging custom AI models to enhance the usage of OpenAI’s general-purpose capabilities. In our opinion, Custom models could offer tailored solutions to specific mapping challenges, potentially improving accuracy and efficiency.

“As the AI landscape evolves, we are also keeping an eye on advancements in computing power, aiming to harness more potent AI capabilities for future projects.”

Conclusion

Gepard AI mapping development shows a real-life example of AI in eCommerce product data management. By integrating OpenAI’s cutting-edge technologies, the Bintime development team has achieved 70% higher efficiencies, reduced manual efforts, and ensured data accuracy, setting new industry standards.

If you are looking for implementation of AI-driven solutions — you can easily partner with us to innovate, automate, and elevate your business to new heights. Contact us today to explore collaboration opportunities.