Modern consumer brands are confronting an uncomfortable truth: the products they once made for retail shelves often fail online. The search behavior, recommendation engines, AI assistants, and fast-moving reviews shape demand more than in-store displays did. So, in this environment, success depends not on a single "hero product" but on a system that truly understands how algorithms and people actually make decisions.
That is the world that Rifat Can Ishakoglu, Co-Founder & CEO of BoldRanks, has built his reputation in. He has led more than 20 brands to enter and scale in the U.S. market, with multiple category leaders and cumulative revenues approaching billions of dollars across his portfolio, during the last decade alone. Lying at the core of his methodology is something he refers to as A2F – Algorithm + AI-Friendly product creation.
From Shelf-First to A2F-First
"Most underperforming SKUs we've seen weren't bad products," Ishakoglu says. "They were simply designed for the wrong world—built for shelves, not for algorithms and AI discovery." Instead of treating e-commerce as a channel to bolt on at the end, BoldRanks starts from how customers search, scroll, compare, and decide online.
A2F is a framework wherein product and content align with marketplace signals: search and rank dynamics, CTR/CVR, ratings velocity, review health, and TACOS guardrails. This structures the copy and metadata so that AI shopping assistants and generative search surface accurate and brand-safe answers. In other words, SKUs are engineered to make sense not just to humans but to the systems that mediate most digital shopping journeys.
What A2F Looks Like in Practice
In practice, A2F touches every part of a launch. Titles, bullets, and visual assets are mapped to intent clusters rather than stuffed with random keywords; packaging, format, and price ladders are tested for their impact on the full funnel from impression to click to purchase, rather than simply chasing a lower CPC. Early-stage review programs are designed to create healthy ratings velocity, knowing both algorithms and people are most sensitive in the first weeks of a product's life.
On the advertising side, Ishakoglu's team sets up guardrails-spend caps, day-parting, and kill-switches-that protect the contribution margin and keep TACOS in a sustainable band. "It's very easy to 'buy growth' for a quarter and destroy the unit economics," he says. "A2F forces us to look at growth and margin together, not in isolation."
A Dual Manufacturing Strategy to Match the Model
If the supply chain cannot support the product strategy, it is not good enough. BoldRanks runs a dual-manufacturing model at the company: textiles manufactured in Türkiye and supplement and beauty products made in the U.S. The logic behind this is rather straightforward. In textiles, Türkiye offers a mix of craftsmanship, lead-time flexibility, and cost reliability that serves marketplace-driven inventory well. In regulated categories like supplements and cosmetics, U.S. manufacturing reinforces trust, compliance, and premium positioning.
This split also serves as hedging against geopolitical risk and tariff shocks. Given that each category is matched to a manufacturing base that best fits its constraints, the company reduces exposure in single countries while keeping the A2F engine supplied with the right products at the right pace.
Pushing the Boundaries: A 3D-Printed Toy Project
One of the more unusual experiments to come out of BoldRanks is a Kickstarter-born 3D-printed toy initiative. Toys are famously China-dependent, with long lead times and heavy reliance on global logistics. Ishakoglu's team wanted to know what would happen if they flipped that script and treated toys as a localized, on-demand category.
They used 3D printing and an A2F lens to build a toy concept around online discovery, rapid iteration, and proximity to the customer. The project compressed feedback loops from months to weeks and showed that even rigid, import-heavy categories have more room for localization than many operators assume.
A Rare Cross-Disciplinary Profile
What makes Ishakoglu rare is the combination of domains in which he operates: he's not only a brand strategist, but also a supply-chain specialist and performance marketer. To colleagues and partners, his work represents a rare blend of product design insight, marketplace and AI algorithm literacy, and cross-border operational know-how.
That blend has made his A2F framework a draw for independent founders and more established companies alike, who are rethinking how they launch and scale products in an Amazon-first world. Many of them arrive after trying fragmented tactics; they stay when they see that A2F gives them a way to measure progress and make better trade-offs across categories, regions, and price points. Beyond services: towards a more integrated operating system. In addition to BoldRanks' branding, IntegTrade by Ishakoglu is an integration-first, single-panel operating layer for SMEs exporting from Türkiye to the U.S., with the aim of reducing time-to-first-sale while growing compliance pass-rates. Early modules are focused on PIM/DAM, compliance workflows for supplements and beauty, marketplace listing, A+ content, landed-cost calculations, ad guardrails, QuickBooks sync, and reimbursement tooling. Importantly, the vision is not to become a merchant or seller of record but to act as the connective tissue between brands and the platforms they depend on. "The legal and commercial responsibility stays with the brand," he says. "Our job is to remove friction so they can execute the A2F playbook more reliably." Setting the benchmark for marketplace-native brands. If there is a unifying theme in Ishakoglu's work, it is that marketplace-native brands need their own operating logic and not some lightly modified version of retail playbooks. For BoldRanks, that logic is A2F: build for algorithms and AI, respect how people really shop online, and back it up with supply chains that can keep up. As more companies confront the limits of shelf-first thinking, the model taking shape in Boca Raton is beginning to look less like an experiment and more like a benchmark.
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