Loops Raises $3.5M Seed to Connect AI Search, Shopping Assistance, and Retail Data

Finding a product online is easy. Deciding whether it is the right one can still mean switching between search results, sizing charts, product pages, and a support conversation that starts from scratch. Loops is betting that retailers need a shared intelligence layer connecting those moments.

Loops has announced a $3.5 million Seed round from a16z speedrun, NTT Docomo, Connecticut Innovations, Alumni Ventures, and Monday Capital. The company plans to use the funding for product development, customer deployment capacity, and international expansion as it builds what it calls an AI operating system for commerce.

The platform brings together search, product discovery, shopping assistance, customer operations, product enrichment, and virtual try-on. Its central proposition is that these capabilities become more useful when they share knowledge of the retailer’s catalog, business context, and customer journey.

Connecting the Pieces of AI Shopping

A retailer can deploy a capable search engine and still leave customers struggling with unanswered questions. It can add a conversational assistant, but that assistant needs accurate product information and context about what the shopper has already considered. Adding another application creates another integration to maintain.

Loops aims to address that fragmentation with one underlying layer. The announcement describes a cycle in which better catalog data improves search, search supplies context to the shopping assistant, and conversations expose missing product information that can be addressed through enrichment.

Consider a shopper looking for an outfit for an outdoor wedding within a budget. Understanding that request requires more than matching the word “wedding.” A useful system needs to interpret the occasion, price limit, style preferences, and product characteristics, then maintain that context when the shopper asks about fit or alternatives. That is the kind of connected experience Loops is pursuing.

The company’s product descriptions explain that AI Search interprets full sentences, occasions, budgets, and constraints. Loops also says results can be ranked around retailer revenue and margin goals. That is a meaningful product-design choice: commercial objectives can influence which suitable items appear first, alongside relevance to the request.

Catalog Data Is the Foundation

Much of the work happens before a shopper asks a question. Product catalogs often contain uneven descriptions and missing attributes. If a garment’s material, fit, or care instructions are absent, a fluent assistant cannot reliably answer questions about those details simply by sounding confident.

Loops’ Product Enrichment capability fills in missing catalog structure, descriptions, and attributes, according to the company. The release presents this as the foundation that helps both search and the assistant understand the inventory. Better structured information can make products easier to retrieve and compare across different interfaces.

The shopping assistant then guides customers through decisions, drawing on catalog information such as stock, sizing, and policies. Loops describes deployment across a retailer’s website, mobile app, and WhatsApp. Its Marketplace Assistant handles product questions on marketplaces, including Trendyol, using the retailer’s catalog and brand voice.

Virtual try-on adds a visual component to that journey. Loops says shoppers can use a photo to see products on themselves before committing. Visual simulation and fit guidance may help people evaluate an item, but the announcement does not establish that either guarantees physical fit or eliminates returns.

The broader technical point is orchestration: coordinating specialized capabilities around shared information. The company has not disclosed enough architectural detail in the announcement to independently assess its model stack, evaluation methods, or how all customer context is maintained across channels.

What the Early Customer Numbers Show

Loops reports more than 70 customers and pilots, millions of shopper experiences handled, and a 64% rate of pilots becoming paying customers. Those figures indicate early commercial traction, although the announcement does not provide the underlying cohort sizes, time periods, or contract values needed to evaluate them in greater detail.

The release names Turkish supermarket chain A101 as a user of the platform for catalog navigation and shopping assistance. In Loops’ published comparison, sessions involving the platform had roughly 83% higher observed purchase conversion than the comparison segment.

That result needs careful interpretation. The company explicitly describes it as observational, rather than a randomized measurement. Shoppers who choose to engage with an assistant may already have different purchase intent from those who do not. The comparison is an encouraging signal to investigate, but it does not establish that Loops alone caused an 83% conversion increase.

Fashion retailer Mavi is another named example, using Loops for AI search and shopping assistance around style, occasion, and fit. These deployments help illustrate the platform’s intended role across different catalogs, while leaving questions about implementation effort and repeatable financial outcomes for future reporting.

A Retail Starting Point for Vertical AI

The company’s origin story reflects the friction it wants to remove. Co-founders Ari Nazir and İlker Selim Zorluoğlu were in Bali when Zorluoğlu tried to purchase a gift for his young daughter, contacted the retailer, and waited hours for a response. The sale never happened. According to the announcement, Nazir built the first prototype over a weekend.

Loops now sees commerce as the first application of a broader strategy: building AI systems deeply around the knowledge, integrations, and workflows of particular industries. Financial services and energy are its next planned areas. It is also developing specialized small-language-model capabilities for tasks where domain knowledge and deployment constraints favor a more focused model.

Those ambitions extend beyond the current retail product and should be understood as development plans. The immediate test is whether Loops can turn its connected shopping experience into reliable, repeatable deployments as its customer base grows.

The Seed funding gives the company resources to pursue that work. Its opportunity rests on a practical retail problem: helping a shopper move from an imperfect description of what they want to a confident purchase, with accurate information and useful assistance throughout.