E-commerce conversion rates hinge on personalized visual content, yet traditional photoshoots cost tens of thousands per session and capture only one model in one setting. This production bottleneck forces fashion and retail brands into a choice between authenticity and budget, leaving them unable to show their products across the diverse body types, skin tones, and ages that reflect their actual customer base. Flock AI addresses this systemic challenge by deploying custom AI models trained on 200+ brand-specific visual attributes to generate unlimited, brand-accurate product imagery across every customer demographic without expanding creative headcount or photoshoot budgets. The platform uses reinforcement learning to continuously improve outputs based on creative feedback and conversion data, with brands using Flock achieving up to 90% cost savings compared to traditional photography and 30%+ conversion rate lifts from more representative imagery. Cofounded by former Bloomingdale’s and Walmart merchandising executive Manvitha (Vita) Mallela and computer vision engineer Malavika Reddy, the company was born from their firsthand frustration with never seeing themselves represented in online shopping experiences.
AlleyWatch sat down with Flock AI CEO and Cofounder Mallela to learn more about the business, its future plans, recent funding round that brings total funding to $7.5M, and much, much more…
Who were your investors and how much did you raise?
We raised a $6M Seed round led by Work-Bench, bringing our total funding to $7.5M. The round included participation from January Ventures, Red Bike Capital, Outlander VC, AI Furnace, and strategic angel investors.
Tell us about the product or service that Flock AI offers.
Flock AI is an AI-native visual commerce platform built specifically for fashion and retail. Acting as a creative team’s co-pilot, Flock allows brands to generate tailored, brand-accurate product imagery for every customer, channel, and context, without expanding headcount or budgets. At the core of the platform is Flock’s brand DNA dataset: more than 200 brand-specific parameters that encode a company’s visual language, style rules, and constraints. Using reinforcement learning and brand-specific AI models, Flock enables one-to-one e-commerce experiences that were previously impossible, while freeing creative teams to focus on the tasks that move the needle. Brands using Flock report up to 90% cost savings compared to traditional photoshoots and 30%+ conversion rate lifts from more
What inspired the start of Flock AI?
Flock was born from a personal frustration my cofounder Malavika Reddy and I both experienced as online shoppers: we never saw ourselves represented in brand imagery.
With my background leading merchandising and creative operations at Bloomingdale’s and Jet.com/Walmart, I’d experienced firsthand how slow, expensive, and limiting traditional content production is. A single photoshoot costs tens of thousands and still only captures one model shot in one way. We realized that AI could solve both problems simultaneously: giving brands unlimited, diverse imagery while making the economics work.
How is Flock AI different?
Most AI image tools are generic and don’t understand brand. Flock is purpose-built for fashion and retail. Each client gets a custom AI model trained on 200+ visual attributes that captures their unique brand DNA. Our system learns what converts through reinforcement learning, continuously improving results using creative feedback and real conversion data. And we do it responsibly, generating imagery that reflects consumers across body types, skin tones, and ages, a level of representation no photoshoot budget could achieve. We fit directly into how creative and merchandising teams already work, so teams spend less time on repetitive production and more time on storytelling.
What market does Flock AI target and how big is it?
We target fashion and retail brands that need to produce high-quality visual content for e-commerce, advertising, and social media.
Brands spend billions annually on photography and visual content production. The broader visual content market represents a massive opportunity as brands shift from traditional photoshoots to AI-powered content generation. We’re seeing enterprise demand accelerate as creative teams realize AI isn’t replacing them, it’s giving them leverage to do more with their time and budgets.
What’s your business model?
Flock operates as a SaaS platform.
How are you preparing for a potential economic slowdown?
When budgets tighten, brands can’t afford photoshoots for every product and every channel, but they still need high-quality visual content to drive e-commerce sales. Flock delivers up to 90% cost savings compared to traditional production, so we become a necessity rather than a nice-to-have. Our enterprise clients are committed into long-term relationships because the ROI is so clear and immediate.
What was the funding process like?
The funding process was really about getting the right conviction from investors. Female-founded companies receive less than 2% of all venture funding, so securing a lead investor with strong conviction in our capability to execute was critical. We are insiders building something we ourselves need, and we needed our lead to understand that. Work-Bench was a natural fit. Once that lead conviction was in place, the rest of the round came together quickly by bringing in strategics who understand the space and our approach.
What are the biggest challenges that you faced while raising capital?
The biggest challenge was helping investors understand that this isn’t just another generic AI image tool. The generative AI space is crowded, and many investors had seen pitches for AI image generation before. We had to clearly demonstrate why fashion requires a specialized, vertical approach: why brand accuracy, product fidelity, and busting biases inherent in AI matter so much in this category. The proof was in the numbers: once investors saw our conversion lift data, cost savings metrics, and the caliber of enterprise brands trusting us with their visual content, the conversation shifted quickly from “why this?” to “how fast can you scale?”
What factors about your business led your investors to write the check?
Three things stood out. First, our traction: Having built and implemented a solution that works for tier-one enterprise clients demonstrated clear product-market fit. Second, our team: the combination of deep retail domain expertise and cutting-edge AI and computer vision capability is rare. I spent years in the trenches at Bloomingdale’s and Walmart understanding the challenges, and Malavika brings over a decade of AI and ML experience to build the solution. Third, the structural shift in our favor: e-commerce success now depends on personalized, high-quality visual content at scale, and traditional photoshoots and first-generation AI tools simply cannot deliver that.
Three things stood out. First, our traction: Having built and implemented a solution that works for tier-one enterprise clients demonstrated clear product-market fit. Second, our team: the combination of deep retail domain expertise and cutting-edge AI and computer vision capability is rare. I spent years in the trenches at Bloomingdale’s and Walmart understanding the challenges, and Malavika brings over a decade of AI and ML experience to build the solution. Third, the structural shift in our favor: e-commerce success now depends on personalized, high-quality visual content at scale, and traditional photoshoots and first-generation AI tools simply cannot deliver that.
What are the milestones you plan to achieve in the next six months?
We’re focused on three priorities: accelerating product development, including scaling our content infrastructure, deepening analytics, and advancing personalization capabilities. Scaling our team across engineering, operations, and sales to meet growing enterprise demand. And expanding our client base while maintaining the service our enterprise customers expect.
What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?
Don’t wait for permission. We started selling before we had a perfect product because we knew the problem was real. Every conversation with a potential customer taught us something that made the product better. New York gives you an unfair advantage because your enterprise customers are right here. Use that proximity. If you can get one customer to pay you and then prove the ROI, the next ten become much easier. Capital follows traction, not the other way around.
What’s your favorite winter destination in and around the city?
K-town for tofu stew.
You are seconds away from signing up for the hottest list in NYC Tech!
Sign up today
