Most experts look at retail technology through consumer expectation and visual gimmicks, mistaking the hype of advertising campaigns for actual commercial application.
By analysing and monitoring fashion tech startups that are replacing these two types of assumptions with factual analysis of design, merchandising, and the supply chain, this guide provides a clear breakdown of how to distinguish factual platform performance from false vendor promises, and the role that these systems will ultimately play in the evolution of retail economics.
The Shift to Business Software
Fashion technology startup markets will be dominated by enterprise software in 2026, not solely direct-to-consumer applications; as such the business of fashion technology has evolved from relying on novelty to concentrating on the resolution of operational challenges (planning, design, and manufacturing) through software solutions that create real value.
By focusing on how these software platforms create value by pinpointing the exact business challenges that they address, it is essential to combine the value of the SaaS solution with funding totals, as well as any existing data systems that the solution integrates with.
In the past, many vendors made generalised statements claiming that they could reduce waste, but now vendors usually provide a hard baseline, as opposed to only saying that they can reduce waste through vague statements of functionality. A list of software platforms that are solely based on what the vendor has stated in their marketing literature is not acceptable to an enterprise buyer.
To determine actual market value, an enterprise buyer should apply the following guidelines:
Deployment Effort: Will the software utilise standard product data, or will an entire system and extensive manual labelling be required?
Financial Impact: Will the software physically reduce returns and prevent wasted sample costs, or is it only a visual gimmick?
Evidence Status: Is the data substantiated by an independent party, or is it merely a claim made by the vendor through a controlled test?
AI Design and Creative Workflows
Traditionally, during the design phase, physical sampling is utilized, costing thousands of dollars, and typically taking weeks to get each item. Generative AI and digital mapping allow brands to test ideas before they cut a single piece of fabric.
1. Raspberry AI
Raspberry AI is a platform that develops generative AI to assist fashion designers with their creative workflow. The goal of Raspberry AI is to speed up the design process by providing designers (and brands) with the ability to create realistic product images from raw, hand-drawn sketches.
The primary market for Raspberry AI is in fashion brands, design teams and large retailers. Brands can now create a digital asset from a flat sketch, allowing them to make timely decisions without having to wait for physical samples.
Funding for Raspberry AI has been strong thus far. They raised $28.5 million in total but most of this came from a $24 million Series A round led by Andreessen Horowitz.
Raspberry AI is endorsed by several major brands including Under Armour, MCM Worldwide, and Kate Spade, based on the findings of independent research that show Raspberry AI has passed the early testing phase and is embedded within many major brand workflows. Raspberry AI's pricing is not available publicly.
2. Refabric
Refabric offers an AI-assisted apparel design and product photography platform that covers the entire workflow from conceptualization to design integration, fabric selection and automated product photography.
The target market for Refabric will include apparel brands and product development teams who need a fast turnaround time from conceptualization to final visual.
Refabric has established partnerships with several major industry participants including LVMH and Sun Tekstil. Public funding reports vary according to source so it's best to verify the exact amounts of financial support directly with the companies.
Market analysts have determined that Refabric provides a complete solution that takes you from concept all the way to collection; the amount of money needed for these services is not known to anyone outside of the company.
3. AI.Fashion
The company AI.Fashion was founded in early 2023 and focuses heavily on creating fashion photography and content using artificial intelligence systems. The virtual photoshoot is their main product offering.
Customers (brands) will have the ability to create high-quality fashion content, but at a fraction of the cost of conventional production methods (economy of scale). The key customer segments for the product are: e-commerce teams, creative agencies, and fashion brands.
The company recently raised a $3.6 million seed round (Feb 2024). Several of the well-known financial publications have followed the progress and success of the company's product, although case studies for enterprise use cases are limited and not widely available to the public. The amount charged for the services is not made public by the company.
4. BLNG
BLNG is a design generator of jewelry and uses generative artificial intelligence to create designs and marketing assets to sell the items created.
Typically, jewellers and design teams are the major buyers of the company's services. The company has raised a total of $4.5 million in the form of equity from various investors from two separate funding rounds, including a $3 million dollar seed round.
The company is not making public the price of the services it offers nor does it publish any detailed information regarding the returns on investment its customers receive.
Predictive Merchandising and Forecasting Systems
The ability to evaluate the correct amount of inventory to procure prior to the start of each season is critical to profitability, and therefore predictive merchandising fashion tech startups develop systems that predict future market demands.
1. Heuritech
Heuritech develops systems that are capable of predicting future markets by means of computer-vision technologies.
The company uses computer-vision technologies to analyze millions of social media images every month to find new and emerging trends in consumer tastes, thereby preventing the introduction of products to the market that consumers would not show interest in or buy.
The target customers for this service are luxury brands, sportswear, beauty brands, etc. Many of its well-known clients include the following luxury brands: Louis Vuitton, Dior, Prada, and New Balance.
This system provides clients with the ability to identify and track over 20,000 trend identifiers and forecast demand for those trend identifiers up to 24 months into the future with 90% accuracy (as reported by the company). However, potential buyers should be aware that this data is self-reported by the company and that providing proof of the same level of accuracy in the fashion industry is extremely challenging.
The company has received over $30 million in funding, and pricing for the service is dependent on an enterprise quotation.
2. Stylumia
Stylumia is a demand sensing and competitive intelligence platform that uses AI to help brands identify signals of demand so they can make better-informed and safer decisions about how to manage their inventory.
Retailers, merchandisers, and planners utilize this platform to determine how much inventory to purchase and how many items to make to fill their inventory.
Sources claim that Stylumia can help businesses reduce their markdowns by up to 30%, which represents a significant reduction in gross margin. However, enterprise buyers must see verified real-world examples of how Stylumia has provided businesses with this type of information before they will be willing to trust this statistic. The pricing for Stylumia is not available to the public.
3. Nextail
Nextail is an AI-driven inventory optimization tool. It gives brands information about what stores they should stock with clothing and when they should replenish their stock in each store.
The primary users of Nextail are fashion retailers and large merchandising teams, who are faced with the problem of determining where to place clothing and when to restock their inventories.
The company raised $18 million in Series B funding. Despite this level of financial backing, detailed metrics and statistics about Nextail's customer base are scarce. This means that Nextail's pricing is not available to the public.
4. Lily AI
Lily AI's software provides the building blocks for product attribution and product intelligence. It helps retailers streamline their product and inventory data, making it easier for consumers to find products.
The company utilizes product labeling to improve the effectiveness of searching or discovering products through their e-commerce platform. The company also targets retailers and other online merchants to enhance their products.
The company has achieved $79.5 million in external funding. Other sources note that usage of the service can increase the relevance of results returned from queries by up to 60%. However, potential customers should verify this claim with an actual live test product. The pricing structure of the software is designed for larger companies and is not available for smaller users.
Fashion Tech Startups for Discovery and Styling
Companies that offer online stores using a typical type of search bar have not been providing adequate services to customers. As a result of this inadequacy, companies that provide a solution to searching and discovering clothing will develop a better way for users to find clothing in real-time using natural language, artificial intelligence, and digital identity.
1. Daydream
The Daydream platform is an interactive shopping assistant that provides real-time support with clothing purchases.
The system is based on the concept of conversational shopping, whereby a user communicates through text messages with an AI-powered fashion expert that suggests products based on the user's preferences.
Daydream intends to cater to both consumers searching for clothing and brands wishing to distribute their apparel.
The Daydream product launched with a significant size catalog. The catalog now exceeds 2 million products from over 8,500 brands. Daydream announced a seed round of $50 million in funding in mid-2024. While consumers can access the service for free, the agreement between Daydream and brands is not publicly disclosed.
2. Phia
The Phia platform combines an AI-powered shopping assistant and a price comparison tool across multiple retailers to assist consumers in locating and comparing fashion-related items and verifying prices at different retailers.
Phia has achieved meaningful growth with over 1 million registered users and more than 6,200 retailer partners.
The company states that brands using Phia's platform reported a conversion rate 13% greater than others and a 50% reduction in returns. The figures reported by companies are self-reported and cannot be verified, so it is essential to evaluate the integration independently for verification. The company just closed its one and only round of financing (Series A) for $35 million. Commercial brand pricing has not been published.
3. Stylitics
Stylitics is focused on AI-enabled styling and outfit creation. Stylitics builds coordinated looks from the base catalog selection of a retailer automatically, making them suitable for purchase.
Retailers and e-commerce brands use this tool to improve their average order values because when you see the complete outfit, you may want to buy everything in the picture instead of just one item.
Stylitics has raised approximately $99.5 million. This size of funding indicates that it is a well-capitalized pure-play styling platform. The return statistics for a brand partnering with Stylitics are confidential. To acquire pricing, you must contact Stylitics.
4. DressX
DressX focuses on creating an all-digital fashion experience by selling virtual garments that have a presence only in digital environments, such as photos or other types of virtual environments.
DressX targets creators (digital fashion users) and brands that want to create a digital fashion presence.
DressX has closed over $17 million in funding to date. DressX is the dominant player in the digital garment market; however, DressX does not have publicly disclosed metrics regarding individual brand partnerships. Pricing is available upon request, and the consumer price for each digital clothing item varies.
Virtual Try-On and Fit Prediction
The problems associated with poor fit are one of the largest contributors to high rates of return for fashion e-commerce. These fashion tech startups are attempting to close the visual gap related to fit in fashion e-commerce.
1. Veesual
Veesual is a platform that uses artificial intelligence technology to provide virtual fitting experiences. Veesual provides a visual, virtual fitting experience on retailers' websites.
Veesual's primary customer base is concentrated in mid-tier and luxury retail markets, especially in Europe. Getting a sense of how a garment fits before you buy it would be the goal for this type of buyer.
Secondary reports show that the total funding the company has received is $62 million.
It is good to ask for confirmation of exact customer conversion lifts before signing a contract since pricing for this company has not been disclosed.
2. True Fit
This company uses consumer and product data to predict what size and fit will work best for a shopper. By combining large amounts of information on consumers and products, True Fit is able to identify the best size for shoppers.
The target market consists of large apparel retailers and e-commerce companies looking to reduce their return rates.
The company has raised $55 million. While True Fit is a well-known company in the industry, it has not publicly shared the details of any customer-specific outcomes for at least the last few months. True Fit only provides enterprise pricing.
3. Doji
This solution allows users to create photo-realistic digital avatars using their own selfies and then virtually try on clothing on their digital avatars.
The target market is consumers and e-commerce companies that would like to create a highly engaging shopping experience.
The company has received considerable media exposure, but it is not publicly documented whether Doji's approach is consistently effective at reducing returns. Pricing is not publicly available.
4. Tryly
This company provides augmented reality virtual try-on solutions. Using the try-on function in conjunction with smartphone cameras and body measurements, Tryly integrates visual clothing into retail websites.
This company has only recently been established and is still in the early stages. They offer a modern product workflow; however, they do not have quantified customer case studies available.
5. Vest Fit
Vest Fit offers smart clothing for all types of individuals and athletes, with motion sensors, software, posture tracking, and vibration feedback.
They're targeting not only fitness users but also sports brands and clinical rehab markets; therefore, although the quality of their architecture is excellent, it is still unknown if they will achieve successful commercial outcomes. Pricing of the garments is also confidential.
Fashion Tech Startups for Supply Chain Traceability
Fashion's supply chain is inefficient and highly polluting. Companies creating circularity and materials-based startups are developing a way to track the life cycle of garments, verify material claims, and minimize physical waste.
1. Haelixa
Haelixa is the only company to offer complete traceability of textiles, using 3D printed physical markers placed inside the fabric for accuracy and ease of identification. This information is utilized by all types of textile manufacturers, luxury brands, and sustainability teams to offer evidence of a fabric's origin, protect against counterfeiting, and provide veracity of greenwashing claims.
To date, Haelixa has raised $7.2 million in funding. Since traceability is becoming a regulatory requirement in many regions, this infrastructure is essential. Pricing information is currently unavailable.
2. NEXIST
NEXIST develops digital product passports, which are digital replicas of the entire lifecycle of a garment, including the sourcing data, the ownership history, resale information, etc.
NEXIST sells to fashion brands, compliance teams, and resellers. Their technology allows them to identify and eliminate counterfeit items and verify ethical sourcing practices. Pricing is available only through custom quotes.
3. Heartbeat Studio
Heartbeat Studio offers an exclusive service for textile reuse. The company takes dead stocks from manufacturers, old uniforms, and unwanted promotional products and repurposes them into new garments.
Their clients are primarily sports clubs, corporations, and organizations that have dead inventory. They are partnered with the Ice Hockey World Cup series and all pricing is based on a specific project.
4. unspun
The startup unspun has created a process that creates custom made-to-order clothes using a process called 3-D weaving. They have created a unique method using a proprietary design system, called Vega, to create custom apparel with a lot less waste from cutting than traditional garment factories.
Their target markets are both apparel companies who want zero waste production and consumers who want clothing made just for them. They directly attack the waste generated by traditional cut-and-sew manufacturing processes, and pricing for enterprise partnerships is custom.
5. SoftWear Automation
The startup SoftWear Automation builds and sells robotic garment manufacturing machines that replace much of the labor involved in sewing and constructing apparel. Their target market is large apparel manufacturing businesses and industrial production facilities.
They have raised $20 million in Series B funding, bringing their total funding to date to $45.6 million. The hardware and robotics involved in building these machines requires very large amounts of investment, which takes a long time to get to market. Therefore, prices are custom set for factories.
The Future of Fashion Technology
The overall conclusion of the fashion technology industry is that it is moving away from using interactive applications, animation, graphics, and CGI to using actual inventory tracking systems as a foundation for the future of technology.
Companies that simply generate artificial intelligence (AI) based on images of clothes without actually connecting to the inventory data of the retailer will be unsuccessful.
The companies that will succeed in this transitional stage of the fashion industry will be those that provide the supporting infrastructure to build on, while not necessarily creating a product of their own.
The true value of the technology is found in systems that minimize the cost of sampling, eliminate unnecessary production, and provide an effective way to attribute product data to enable search functionality. In the future, enterprise customers will not be able to depend on vendors to create tools that "will change the industry."
Instead, enterprise customers need to demand data from vendors on how they did reduce markdown rates from 30% to 15%.
Common Questions About Fashion Technology Platforms
What is the biggest problem with virtual try-on platforms?
The primary point of failure for all virtual try-on systems is inaccurate data input. Virtual try-on systems rely on perfect representation of product images and accurate garment measurements in order to work correctly.
Most retailers have consistency issues with their product catalogs. If a retailer forces the use of complex AI virtual try-on systems using poor-quality photography, inaccurate size information, or both, then the results will not only fail to be realistic. The consumers will have less confidence in the retailer's products, which will lead them to return them.
How will digital product passports change the market?
Digital product passports convert an environmental marketing claim into a legally enforceable industry standard. When brands are required to attach a digital equivalent to a physical product, this digital product passport provides an accurate mapping of the physical product by creating a digital equivalent that includes detailed information about the material source(s), composition, and ownership chain of a product.
Digital product passports fundamentally alter how the resale industry functions by allowing consumers to verify the authenticity of a product without requiring expensive manual verification. Digital product passports will accelerate the growth of the second-hand market while ensuring that primary manufacturers maintain their environmental responsibilities in relation to the original production process.
Where do retail technology projects lose the most money?
The primary area of capital loss occurs during the proof-of-concept stage of a technology implementation, where tools are not integrated directly into an existing ERP or PLM system. This often occurs when retail brands purchase technology that provides AI-based design capabilities (design), but are then required to manually export and email their files to the production facility.
If a technology solution creates a new process rather than replacing an existing process, the retailer will incur both the cost of the technology solution and the costs associated with manual labor associated with creating and sending files.
How can buyers check if an AI platform's forecasts are accurate?
Enterprise buyers must define a baseline for the metric or variable in question. If an AI vendor claims it can produce an accuracy rating of 90%, the enterprise buyer should ask: "what exactly is the 90%?"
An enterprise buyer should require that the vendor run a historical analysis using its own historical sales data for the preceding year. The enterprise buyer must compare the actual sales from the preceding year with the vendor's forecasts of sales for the same period.
As always, do not accept general marketing claims from the vendor; only accept a vendor's specific mathematical evidence with respect to your own company's financial records.