Introduction
The retail landscape is undergoing a seismic shift as artificial intelligence moves from a niche research tool to a core driver of commerce. In the past decade, retailers have experimented with chatbots, recommendation engines, and automated inventory systems, but these solutions have largely been siloed, proprietary, or limited in scope. The launch of Kepler by New Gen marks a pivotal moment in this evolution. Kepler is the first intelligence layer designed specifically for retailers, giving them granular control over how AI perceives and interacts with their product catalogs. By coupling this layer with the industry’s inaugural AI Agent Score, Kepler provides a comprehensive framework for measuring and improving agent performance on retail sites.
This post explores the significance of Kepler, the mechanics behind its AI Agent Score, and the practical implications for retailers looking to adopt agentic commerce. We’ll examine how Kepler redefines the relationship between data, AI, and the customer experience, and we’ll consider the challenges and opportunities that come with deploying an AI‑ready infrastructure at scale.
Main Content
The Architecture of Kepler
Kepler is built on a modular architecture that sits between a retailer’s existing e‑commerce platform and the AI agents that drive customer interactions. At its core, Kepler ingests product metadata, customer behavior logs, and contextual signals such as seasonal trends or inventory levels. It then normalizes and enriches this data into a unified knowledge graph that AI agents can query in real time.
Unlike traditional recommendation engines that rely on static collaborative filtering, Kepler’s graph‑based model allows agents to understand relationships between products, attributes, and customer intents at a semantic level. For example, if a shopper is browsing “wireless earbuds,” Kepler can surface not only related audio accessories but also contextual information such as “best battery life” or “noise cancellation,” depending on the shopper’s prior interactions. This level of contextual awareness is what New Gen refers to as “agentic commerce”—the ability of AI agents to act autonomously, making decisions that align with both business goals and individual customer preferences.
The AI Agent Score: Benchmarking Performance
One of the most compelling innovations introduced by Kepler is the AI Agent Score. This metric provides a standardized way to evaluate how well an AI agent performs across a range of key performance indicators (KPIs) such as conversion rate, average order value, customer satisfaction, and time‑to‑resolution for support queries.
The score is calculated through a multi‑layered algorithm that first normalizes raw data against industry benchmarks, then applies weighted factors that reflect a retailer’s strategic priorities. For instance, a luxury fashion retailer might weight brand consistency higher than a discount retailer that prioritizes volume. By offering a transparent, comparable metric, the AI Agent Score empowers retailers to identify underperforming agents, fine‑tune their models, and ultimately drive better outcomes.
Real‑World Use Cases
Consider a mid‑size electronics retailer that has recently integrated Kepler into its online storefront. Prior to Kepler, the retailer relied on a generic recommendation engine that suggested products based on purchase history alone. After deploying Kepler, the AI agents began to factor in real‑time inventory levels, upcoming product launches, and even weather data to recommend items that were more likely to resonate with the shopper’s current context.
Within three months, the retailer reported a 12% increase in conversion rate and a 9% lift in average order value. The AI Agent Score, which started at 68 out of 100, climbed to 82 after iterative tuning based on the score’s feedback loops. These gains illustrate how Kepler’s intelligence layer can translate into tangible business metrics.
Another example involves a fashion retailer that uses Kepler to power a virtual stylist chatbot. The chatbot can now suggest outfits that not only match the customer’s style profile but also incorporate sustainability metrics, such as the carbon footprint of each garment. By aligning product recommendations with the retailer’s sustainability commitments, the chatbot helped the brand increase engagement with eco‑friendly product lines by 15%.
Challenges and Considerations
Deploying an AI‑ready infrastructure is not without its hurdles. Data quality remains a critical concern; Kepler’s effectiveness hinges on the completeness and accuracy of the underlying product metadata. Retailers must invest in data governance practices to ensure that product attributes, pricing, and availability are consistently updated.
Another challenge lies in the interpretability of AI decisions. While Kepler’s graph model offers more transparency than black‑box neural networks, stakeholders still need tools to audit agent behavior. New Gen addresses this by providing dashboards that trace the decision path of each recommendation, allowing product managers to verify that the AI is adhering to brand guidelines.
Finally, the AI Agent Score, while powerful, can be a double‑edged sword. Over‑optimization for the score might lead retailers to prioritize short‑term metrics at the expense of long‑term brand equity. It is essential for organizations to balance the score’s insights with a holistic view of customer experience and brand strategy.
The Future of Agentic Commerce
Kepler’s launch signals a broader trend toward agentic commerce, where AI agents are not just tools but partners in the retail journey. As natural language processing and multimodal AI continue to mature, we can expect agents to handle increasingly complex tasks—from dynamic pricing negotiations to personalized content creation.
Retailers that adopt Kepler early will likely gain a competitive advantage by being able to iterate quickly on agent behavior, benchmark against industry standards, and align AI initiatives with business objectives. Moreover, the modular nature of Kepler means that new features—such as augmented reality try‑on or voice‑activated shopping—can be integrated without overhauling the entire system.
Conclusion
Kepler represents a significant leap forward in the way retailers harness artificial intelligence. By providing a unified intelligence layer that contextualizes product data and by introducing the AI Agent Score as a benchmark for performance, New Gen equips retailers with the tools they need to build truly agentic commerce experiences. The real‑world successes seen by electronics and fashion retailers underscore the tangible benefits of adopting this technology. While challenges such as data quality and interpretability remain, the potential for increased conversion rates, higher average order values, and deeper customer engagement is undeniable.
As the retail industry continues to evolve, Kepler offers a roadmap for integrating AI in a way that is both scalable and aligned with business goals. Retailers who embrace this approach will not only stay ahead of the curve but also redefine the customer journey for the next generation of shoppers.
Call to Action
If you’re a retailer looking to elevate your e‑commerce platform with cutting‑edge AI, consider exploring Kepler’s capabilities. Reach out to New Gen for a personalized demo and discover how the AI Agent Score can help you benchmark and optimize your agents. By partnering with a forward‑thinking infrastructure provider, you can unlock new levels of customer engagement, operational efficiency, and revenue growth. Don’t let your competitors outpace you—start building agentic commerce today.