Hajra Naz
While generative AI has captured attention with its consumer-focused applications, a new wave of retail tech startups is harnessing artificial intelligence to tackle fundamental business challenges. These companies, recently featured in RETHINK Retail’s inaugural “Top AI Leaders in Retail for 2025” list, highlight where retail technology is headed and which challenges are most pressing for retailers today.
Here are four key problems these startups are addressing and how their solutions are reshaping retail operations.
1. Bridging the Consumer-Product Language Gap
The discrepancy between how people search for things and how sellers describe them is a significant problem in the retail industry. Missed sales and decreased engagement result from this misalignment, particularly as buying becomes more diverse across social media, marketplaces, and traditional e-commerce.
Two startups are taking different approaches to solving this issue:
- Lily AI: Focusing on mid-market and endeavor retailers, Lily AI includes consumer-centric terminology in product catalogs to improve item discoverability. Retailers who utilize Lily AI claim striking picks up in activity, advertisement impressions, and income by utilizing organized information and client insights.
- Vody: Vody improves search translation in real-time or maybe optimizes the catalog. Customers may discover what they’re looking for much obliged to Vody’s understanding of cultural settings and patterns through the utilization of multimodal generative AI. For example, a search for a “Taylor Swift jersey” would yield a Travis Kelce jersey, accurately deciding the expectation of the client.
2. Optimizing Inventory with AI
Traditional inventory management frequently uses antiquated push models and manual procedures, which results in waste and inefficiency, especially in the sale of groceries and clothing.
- Nextail: Using automation and AI-driven demand forecasting, Nextail is transforming fashion retail inventory decisions by moving away from intuition-based stockings and toward hyper-localized, data-driven choices. Brands can minimize overstocking and enhance inventory allocation by analyzing fashion-specific patterns.
- Cognitiwe’s WeFresh: Targeting supermarket businesses, WeFresh employs computer vision driven by AI to continuously monitor the state of fresh food. This enables supermarkets to minimize spoiling, optimize restocking, and modify prices without having to make extra hardware investments.
Read More: Why Agentic AI is the Future of E-Commerce Instead ChatGPT
3. Reimagining Pricing and Promotions
A lot of retailers use general discounting techniques that lower profits without increasing sales. More accurate, automated pricing and promotional techniques are now made possible by AI.
- Quicklizard: This technology for dynamic pricing automates pricing choices for extensive product catalogs. Retailers like Sephora and John Lewis can optimize pricing strategies in real-time thanks to their AI-driven methodology, which evaluates price elasticity, competition behavior, and seasonality.
- RevLifter: With an emphasis on mid-market dealers, RevLifter is revolutionizing marketing strategies by exchanging from ordinary reducing plans to more customized, artificial intelligence-powered rewards that raise conversion rates while protecting benefits.
4. Automating Creative Content Generation with AI
The growing demand for visual content across multiple channels has created bottlenecks in content production for fashion and retail marketing.
- Fashionable: This AI-powered platform generates photorealistic fashion imagery, allowing brands to test market responses before production. By reducing sample waste and accelerating product launches, Fashable streamlines the concept-to-market workflow.
- Rocketium: Aimed at enterprise brands, Rocketium automates content creation for digital advertising. Its AI-driven system scales marketing creatives across social, display, and retail media, improving efficiency without increasing marketing team sizes.
Read More: Best AI Tools for Pakistani E-Commerce: Features & Pricing
The Future of Retail Tech
These businesses’ innovations signal a change in retail AI from applications aimed at consumers to operational transformation. While Amazon’s Rufus and other AI-powered shopping assistants create headlines, AI solutions that tackle core retail issues are the true game-changers.
For retailers investing in AI, these startups offer a strategic framework:
- Language optimization for improved product discovery
- AI-powered inventory management for efficiency
- Data-driven pricing and promotions for revenue growth
- Automated content generation for marketing scalability
These innovations are moving beyond simple automation to solving previously intractable problems. Whether it’s Lily AI bridging the language gap or WeFresh reducing fresh food waste, AI is reshaping retail with tangible, business-focused solutions. The future of retail tech lies in these targeted, impactful applications rather than in generic AI hype.
