While generative AI has dominated headlines with flashy consumer applications, a new generation of retail tech startups is using artificial intelligence to solve fundamental business challenges that have long plagued the industry. These companies, recently recognized in the ‘startup’ section of in RETHINK Retail’s inaugural “Top AI Leaders in Retail for 2025” list, point to where retail technology is headed – and which problems are most urgent for retailers to solve.
Here are the four key challenges these startups are tackling, and how their solutions could reshape retail operations:
1. AI Bridges the Consumer-Product Language Gap
The disconnect between how retailers describe products and how consumers search for them represents a multibillion-dollar problem in lost sales and missed opportunities. This gap is particularly acute as shopping becomes more fragmented across channels, from marketplaces to social media to traditional e-commerce sites. Two companies are taking distinctly different approaches to solving this challenge.
Lily AI, which serves mid-market and enterprise retailers, addresses what founder Purva Gupta calls “the missing consumer narrative.” After interviewing over 1,000 women about their shopping motivations, Gupta discovered that consumers describe purchases with emotional detail and unique perspectives – elements typically missing from product descriptions. The company’s Product Content Optimization platform systematically enriches entire product catalogs with consumer-centric language and attributes, creating a structured data layer that powers both frontend discovery and backend operations. Currently focused on fashion, beauty, and home decor categories, Lily AI reports that clients see double-digit increases in sales, ad impressions, and site traffic through improved product discoverability.
Vody takes a different approach, focusing on real-time search interpretation rather than catalog enrichment. The company targets enterprise retailers looking to enhance product discovery through multimodal generative AI that understands current cultural context and trending topics. “Our models understand how customers search, speak, and shop,” explains CEO Stephanie Horbaczewski.
For example, if a customer searches for a “Taylor Swift jersey,” they’re likely looking for Travis Kelce’s jersey – Vody’s data ensures they get the right results. While Lily.ai focuses on building better product data infrastructure, Vody specializes in interpreting and understanding search intent in the moment, helping retailers capture high-intent traffic without requiring constant manual updates to their product data.
2. Optimizing Inventory With AI
Traditional retail operations often rely on outdated “push” models and manual processes, leading to inefficiencies in inventory management and significant waste in fresh food categories.
Nextail is tackling a fundamental disconnect in fashion retail: while consumer behavior and product lifecycles have become increasingly dynamic, inventory decisions remain largely static and intuition-based. “Despite being a multi-trillion dollar industry, fashion mostly runs on an operating model built decades ago,” notes Joaquín Villalba, Nextail’s co-founder and former Head of European Logistics at Inditex.
The company’s platform uses AI to transform traditional top-down ‘push’ inventory models through hyper-localized demand forecasting and automated decision-making. Unlike generic retail solutions, Nextail analyzes the unique patterns of fashion retail – from short product lifecycles to complex size curves – to make store-specific stocking decisions. This helps retailers who previously relied on high-level sales data and manager intuition to make more precise, data-driven inventory allocations.
While fashion retailers grapple with seasonal inventory challenges, grocery chains face an even more time-sensitive inventory problem: fresh food waste.
Cognitiwe’s WeFresh platform addresses this €4 billion-plus challenge in Europe alone. The solution targets enterprise-level grocery retailers and supermarket chains where fresh food management is complex and waste reduction is a priority.
Rather than relying on manual checks and fixed expiration dates, WeFresh uses AI-powered computer vision to continuously monitor fresh food conditions in real-time through existing store cameras. This allows retailers to act proactively – adjusting prices, optimizing restocking, and rotating products before spoilage occurs – without requiring expensive new hardware or sensors.
3. Reimagining Pricing and Promotions
Traditional approaches to pricing and promotions often rely on manual processes and broad discounting strategies that erode margins without maximizing revenue potential.
Quicklizard’s dynamic pricing platform, serving medium to large retailers managing thousands of SKUs, automates pricing for entire product catalogs. “Most retailers can only optimize 10-15% of their catalog using manual methods,” explains Anat Oransky Lev, VP Marketing. The company’s open AI platform allows retailers to implement any pricing strategy through simple Python code, while machine learning modules analyze factors like price elasticity, competitor behavior, and seasonality. This automated approach enables retailers to optimize pricing across their entire catalog in real-time, rather than the 10-15% typically managed through manual methods.
The platform serves notable clients like Sephora and John Lewis & Partners, as well as direct-to-consumer brands generating tens of millions in annual revenue.
RevLifter, targeting mid-market retailers, is rethinking how retailers use promotions. “Promotions have been a retail tactic for around 150 years,” notes Dan Bond, VP of Marketing. The company sits between basic promotion platforms and more expensive enterprise technologies.
4. Automating Creative Content Generation with AI
The increasing demand for visual content across channels has created new bottlenecks for retailers, particularly in fashion and marketing contexts.
Fashable, serving mid-market and enterprise retailers, generates photorealistic imagery that transforms fashion industry workflows from concept to market. The platform creates AI-generated fashion imagery that remains exclusive to the brand, allowing retailers to test market response before committing to physical production while reducing sample waste and accelerating time to market.
While Fashable focuses on product visualization, retailers face another content challenge: creating and adapting marketing creative across an ever-expanding array of digital channels.
Rocketium addresses this scaling challenge for enterprise brands advertising on social, display, and retail media platforms. Its platform automates the creation of multiple creative versions for each campaign element while predicting performance potential, eliminating the manual work of adapting content for different platforms and placements. Built specifically for retail advertisers, it handles everything from version creation to platform compliance, helping brands scale their creative output without expanding their teams.
The Future of Retail Tech
The solutions these startups are developing signal an important shift in retail AI. While much attention has focused on consumer-facing AI applications like Amazon’s recently launched Rufus shopping assistant, these companies demonstrate how artificial intelligence can transform core retail operations. As I recently wr The Future Of AI Shopping, Warts And All”], tools like Rufus are just the beginning of how AI will reshape retail – the real transformation is happening behind the scenes.
For retailers evaluating where to invest in AI capabilities, these startups provide a useful framework: language optimization for discovery, intelligent inventory management, dynamic pricing and promotions, and automated content creation. Each represents an area where AI can solve specific, measurable business challenges rather than simply adding technological sophistication.
What’s particularly notable is how these solutions are evolving beyond simple automation. Whether it’s Lily.ai bridging the language gap between merchants and consumers, or WeFresh predicting fresh food spoilage before it occurs, these platforms demonstrate AI’s ability to solve problems that were previously intractable through traditional methods. While chatbots and recommendation engines may capture headlines, the future of retail technology lies in these focused solutions that deliver concrete business impact.
