4 problems these beginnings he is solving

While the generative one has dominated the titles with glowing consumer applications, a new generation of retail technology is using artificial intelligence to solve the underlying business challenges that have long destroyed the industry. These companies, recently known in the ‘Beginning’ section on the inauguration list of Rethink Retail “Top He Leaders for Sale for 2025”, show where retail technology is directed – and what problems are more urgent for retail sellers to solve.

Here are the four main challenges these startups are being addressed, and how their solutions can reshape retail operations:

1. He bridges Consumer product language gap

The detachment between the way retailers describe the products and how consumers seek for them is a multi -billion dollar problem in lost sales and lost opportunities. This gap is especially sharp as the purchase becomes more fragmented in channels, from market countries to social media to traditional e -commerce countries. Two companies are taking significantly different access to solving this challenge.

Lily AI, which serves the retail of the middle market and the enterprise, addresses what founder Purva Gupta calls “the narrative of the missing consumer”. After interviewing over 1,000 women about their purchasing motivations, Gupta revealed that consumers describe purchases with emotional details and unique perspectives – elements that are usually missing from product descriptions. The company’s content optimization platform systematically enriches catalogs of products with customer -centered language and attributes, creating a structured layer of data that strengthens both the detection of the front and the backend operations. Currently focused on the categories of fashion, beauty and home decor, Lily he reports that clients see double -digit growth in sales, advertising impressions and site traffic through improved product detection.

Vody takes a different approach, focusing on interpreting real -time research rather than enriching the catalog. The company aims at retail sellers of enterprises seeking to improve product detection through multimodal generating that understands the current cultural context and trend topics. “Our models understand how clients look for, talk and buy,” explains CEO Stephanie Horbaczewski.

For example, if a client requires a “Jersey Taylor Swift”, it is likely to look for Travis Kelce shirt – Vody data ensure that they get the right results. While lily.Ai focuses on building better product data infrastructure, Vody specializes in interpreting and understanding the purpose of the moment’s search, helping sellers capture high traffic without seeking continuous manual updates in data of their product.

2. Inventory optimization with him

Traditional retail operations often rely on outdated “push” models and manual processes, leading to inefficiency in inventory management and important waste in fresh food categories.

Nextail is dealing with a fundamental detachment in the retail of fashion: while consumer behavior and product life cycles have become increasingly dynamic, inventory decisions remain largely static and intuitive -based. “Despite being a multi-dollar industry, fashion mainly goes into an operating model built decades ago,” notes Joaquín Villalba, co-founder of Nextail and former head of European logistics in Inditex.

The company’s platform uses it to transform traditional traditional inventory models by anticipating hyper-localizing demand and automated decision-making. Unlike overall retail solutions, Nextail analyzes unique fashion retail models – from short -lived product life cycles to complex size curves – to make specific decisions for store shops. This helps retail retailers who previously relied on high -level sales data and manager’s intuition to make more accurate allocations directed.

While fashion retailers deal with seasonal inventory challenges, food chains face an even more sensitive problem of sensitive inventory in time: fresh food waste.

Cognitiwe’s WEFRESH platform addresses this challenge € 4 billion plus in Europe alone. The solution aims at the retail of the enterprise level and supermarket chains where fresh food management is complex and landing is a priority.

Instead of relying on manual controls and fixed expiration dates, Wefresh uses the vision of the computer with it to constantly monitor fresh food conditions through existing store cameras. This allows retailers to proactively operate – price adjustment, resetting optimization and rotary products before decay occurs – without looking for expensive new equipment or sensors.

3. Refining prices and promotions

Traditional price and promotional approaches often rely on manual processes and extensive deduction strategies that erode boundaries without maximizing the potential of income.

Quicklizard’s dynamic price platform, serving medium -sized retail to manage thousands of squads, automates prices for all product catalogs. “Most retail sellers can only optimize 10-15% of their catalog using manual methods,” explains Oransky Lev, VP marketing. The company’s open platform allows retailers to implement any price strategy through the simple Python code, while machinery learning modules analyze factors such as price elasticity, competition behavior and seasonality. This automated approach enables retailers to optimize prices across their catalog in real time, rather than 10-15% usually managed through manual methods.

The platform serves prominent customers such as Sephora and John Lewis & Partners, as well as direct brands against consumers generating tens of millions of annual income.

Revlifter, aiming at the retail sellers of the middle market, is thinking about how retailers use promotions. “The promotions have been a retail tactic for about 150 years,” notes Dan Bond, Marketing VP. The company sits between basic promotion platforms and more expensive enterprise technologies.

4. Automation of generating creative content with him

The growing demand for visual content in the channels has created new obstacles for retail sellers, especially in fashion and marketing contexts.

Fascable, serving medium market retailer and enterprises, generates photorealist images that convert the flow of fashion industry from concept to market. The platform creates fashion images created by the one that remains exclusive to the brand, allowing vendors to test the market response before engaging in physical production while reducing sample waste and accelerating the market time.

While fascable focuses on visualizing the product, retailers face another content of content: creating and adapting creative marketing into an ever -expanding group of digital channels.

Rocketium addresses this scaling challenge for enterprise brands that advertise on social, screen and retail media platforms. Its platform automates the creation of numerous creative versions for each element of the campaign while anticipating the performance potential, eliminating manual content of content adaptation for different platforms and settings. Specially built for retail advertisers, it addresses everything, from creating the version to the compliance of the platform, helping brands escalate their creative production without expanding their teams.

The future of retail technology

Solutions that these startups are signaling a significant shift in retail. While much attention has been focused on the AI ​​applications faced with the consumer as a recently launched Rufus purchases in the Amazon, these companies demonstrate how artificial intelligence can transform fundamental retail operations. While I have recently made the future of he’s purchases, warts and all “]tools like rufus are just the beginning of how it will reshape retail – true transformation is happening behind the scenes.

For retailers who appreciate where they can invest in his abilities, these startups offer a useful framework: language optimization for detection, intelligent inventory management, dynamic prices and automated content promotions. Each represents an area where it can solve specific, measurable business challenges rather than just adding technological sophistication.

What is especially evident is how these solutions are evolving beyond simple automation. Whether it is Lily.He wishing the language gap between traders and consumers, or Wefresh that predicts the breakdown of fresh food before it happens, these platforms demonstrate the ability to solve problems that were previously interactive through traditional methods. While the recommendation chatbots and engines can capture titles, the future of retail technology lies in these concentrated solutions that give concrete business impact.