Building the AI Agent Layer for E-Commerce: Interview with Federico Sargenti, Co-CEO of CommerceClarity

What does it take to turn one of e-commerce’s most persistent pain points into a scalable business solution? For Federico Sargenti, Co-Founder and Co-CEO of CommerceClarity, the answer lies in lived experience, a deep engineering mindset, and a clear-eyed view of where the industry is headed.An engineer by training, Sargenti has spent over 15 years focused on turning complexity into scalable systems across e-commerce, technology, retail, and operations. 

CommerceClarity is built to be the “agent layer” that plugs into that messy reality and makes catalog operations smarter — without requiring companies to rebuild their stack from scratch. In 3–5 years, Sargenti’s ambition is for CommerceClarity to become the default system that turns raw product data into structured, validated, market-ready intelligence across every discovery surface — marketplaces, D2C, retail partners, and beyond.

In this exclusive interview conducted by Eqvista, Federico Sargenti shares the story behind CommerceClarity, the hard-won lessons that shaped it, and his vision for the future of commerce infrastructure.

Federico Sargenti, could you start by sharing a bit about your background before founding the company?

I’m an engineer by training, and I’ve spent the last 15+ years at the intersection of e-commerce, technology, retail, and operations, always focused on turning complexity into scalable systems.

I started my career building and operating digital businesses, then joined Amazon, where I learned what “industrial-grade” looks like when it comes to structured product data, governance, and execution at scale. After Amazon, I became CEO of Everli and had the opportunity to lead hypergrowth: scaling revenue from €1M to €100M, expanding internationally, and growing the team from a small core group into a 300-person organization across Europe. Along the way, I also co-founded and invested in other ventures in the space of commerce.

Your background at Amazon is particularly relevant here. Amazon has been at the forefront of catalog automation and structured data for two decades. What lessons from Amazon’s approach to product data have you brought to CommerceClarity, and where do you think even Amazon has gaps that you’re addressing?

First, product data is not “content.” It’s infrastructure. When your catalog is structured, governed, and continuously validated, everything downstream improves: discovery, conversion, logistics, customer trust, and ultimately profitability. Second, automation only works when it’s paired with strong standards—taxonomy, quality rules, and clear ownership of data.

Where we think there’s a gap—even for Amazon—is that most of the world doesn’t operate inside one vertically integrated ecosystem. Brands and retailers live in a multi-source, multi-market, multi-channel reality, with ERPs, PIMs, suppliers, PDFs, portals, and legacy processes that don’t talk to each other. And now we’re entering a new era where discovery is increasingly mediated by AI systems, not just traditional search.

CommerceClarity is built to be the “agent layer” that plugs into that messy reality and makes catalog operations AI-native—without requiring companies to rebuild their stack from scratch.

You scaled Everli from €1M to €100M in revenue and built a 300-person team across Europe. What made you decide to step away from that success and start CommerceClarity?

Leading Everli was an incredible journey—hypergrowth teaches you a lot about what truly breaks at scale. One lesson stood out: catalog operations remain one of the most painful, manual, and underestimated bottlenecks in e-commerce.

After years of scaling, I realized the problem wasn’t a lack of effort or talent. It was that the underlying “operating system” for product data is still fragmented: spreadsheets, copy-paste, supplier back-and-forth, inconsistent attributes, and slow time-to-market. Teams end up spending their best people on low-leverage work.

At the same time, the market is shifting quickly. As AI agents start influencing what people buy, unstructured catalogs become a visibility risk. That combination—an unsolved operational pain plus a major platform shift—was the trigger. I wanted to build the infrastructure I wish I had as an operator: something that compounds efficiency and unlocks growth, not just another tool.

You’ve already secured impressive clients in your first year—Nestlé Efarma, Cisalfa, 1000Farmacie. How are you approaching go-to-market? Are you selling primarily to retailers, brands, or both?

We’re intentionally built for both—because the pain sits on both sides of the value chain.

Retailers struggle with multi-supplier complexity: onboarding and standardizing thousands of products coming in different formats, ensuring compliance, keeping taxonomy consistent, and publishing fast. Brands struggle with channel proliferation: distributing content across dozens of retailers and marketplaces while maintaining brand voice, correct attributes, and local requirements.

Our go-to-market reflects that. We typically start with a high-impact wedge—one business unit, category, or workflow where catalog pain is acute—prove ROI quickly, and then expand across categories, markets, and channels. The strongest motion is “land with catalog operations, expand into a broader agent network” as customers see what happens when data becomes structured and enforceable.

CommerceClarity automates catalog ingestion from ERPs, suppliers, and PDFs into structured content. What real-world chaos from your e-commerce days inspired this AI agent approach?

If you’ve ever run an e-commerce operation at scale, you know the reality: suppliers send Excel files, CSVs, PDFs, images, sometimes just an EAN list—and every source has different naming conventions, missing attributes, inconsistent categories, and varying quality.

In practice, that means your team becomes a “human ETL pipeline.” They chase missing fields, normalize specs, rewrite titles, fix taxonomy, and manually adapt content for each channel. It’s slow, error-prone, and it never ends—because assortments change daily.

That’s exactly what inspired the AI agent approach. We didn’t want to automate one narrow step; we wanted to automate the *full loop* of catalog operations: ingest, structure, enrich, validate, and publish. The key is that agents can do the repetitive work continuously, while humans stay in control through rules, approvals, and governance.

With customers like Nestlé Efarma and Arcaplanet seeing 90% cost cuts and 30% sales lifts, how does the Catalog Agent enforce brand rules while enriching data for markets?

The Catalog Agent is designed around a simple principle: enrichment without governance is noise.

So we enforce rules at multiple levels. First, we align every item to the customer’s taxonomy and mandatory attributes—what “good” looks like is defined by the business, not by the model. Then we run validation and quality checks before anything ships: completeness, consistency, formatting rules, channel requirements, and category-specific constraints.

On top of that, we support workflows where teams can approve changes selectively (for example, regulated categories or high-impact claims), while letting lower-risk updates publish automatically based on rules.

The result is that customers get both speed and consistency: the data becomes richer and more localized for each market, but still stays inside brand and compliance boundaries.

From 40+ enterprise clients across petcare, sports, and pharma, what’s the most surprising result you’ve seen in deployments so far?

The most surprising outcome is how quickly fixing catalog structure translates into measurable commercial impact—not just operational savings.

When taxonomy is corrected, attributes are completed, and product copy becomes consistent and searchable, you don’t just reduce workload; you improve discoverability and conversion. In some deployments, the lift shows up faster than teams expect because search, recommendations, and filters start working the way they were always meant to.

The second surprise is what I’d call “AI readiness” becoming real. We’re already seeing cases where better-structured product information helps brands show up in new discovery surfaces—LLM-driven and conversational experiences—not just classic SEO.

With €2.7M funding and offices in Milan, Rome, and London, how will CommerceClarity expand its AI infrastructure globally next?​

First, we’ll keep investing in the core multi-agent platform: making ingestion more universal across formats and systems, strengthening enrichment quality by vertical, and expanding validation and governance so enterprises can automate safely.

Second, we’ll broaden integrations and channel coverage so customers can plug CommerceClarity into their existing stack—ERP, PIM, e-commerce platforms, and syndication destinations—without large migration projects.

Third, we’ll use London as a lever for English-speaking markets. International expansion isn’t just commercial; it’s also about building multi-market, multi-language capabilities and ensuring the platform can support different regulatory and channel requirements globally.

What’s your vision for CommerceClarity over the next 3-5 years? Where do you see the biggest growth opportunities, and what challenges keep you up at night?

In 3–5 years, I want CommerceClarity to be the default “agent layer” for commerce operations: the system that turns raw product data into structured, validated, market-ready intelligence across every discovery surface—marketplaces, D2C, retail partners, and AI-mediated channels.

The growth opportunity is massive because the number of channels is exploding and discovery is shifting. The winners will be companies that can run fast without breaking governance, and that can make their products understandable to both humans and machines.

The biggest challenge is trust and control. Agentic systems must be reliable, auditable, and safe—especially in regulated verticals. The bar for accuracy, compliance, and data governance will keep rising, and we take that seriously. The other challenge is staying ahead of how discovery changes, because platforms and AI interfaces evolve quickly.

Do you have any advice for other founders contemplating the jump from successful operator to startup founder, or for e-commerce leaders evaluating whether to adopt agentic AI systems?

For operators becoming founders: start from a problem you’ve lived, not a trend you’ve read about. Your unfair advantage is pattern recognition from a real scale. Pick a wedge that’s painfully clear, define success metrics early, and build a team that can execute with speed and discipline. Also—be ready to relearn everything. The context changes when you go from optimizing an existing machine to building one from scratch.

For e-commerce leaders: treat agentic AI like critical infrastructure, not a side experiment. Start with a contained, high-ROI process (catalog is a great one), insist on governance and auditability, and integrate with your existing stack rather than ripping everything out. Measure outcomes in time-to-market, cost, and revenue—not just “AI adoption.”