Two friends who first sold hand sanitizer on a high‑school laptop have turned their pandemic‑era side hustle into a Silicon Valley‑backed artificial‑intelligence startup. Nurtilek Raimzhanov and Zuhayr Abdullazhanov, both 21, founded Siml after a series of scrapers and a personal‑finance app taught them the grind of online retail. The Kyrgyzstan‑born duo left the country to study engineering at top U.S. universities, dropped out, and focused on building an AI that does more than assist—it takes custody of an entire store’s operations.
Siml treats a shop as a single autonomous control loop. Its agents maintain a unified catalog graph that resolves listings, variants, supplier records and order histories into canonical products. From that shared data source, the system drafts product descriptions, runs ad campaigns, answers every customer inbox, and even adjusts pricing in real time, attaching reasoning to each change. The execution layer wraps every irreversible action—refunds, price updates, inventory moves—in a merchant‑defined permission model, simulating outcomes and pausing anything that exceeds a risk threshold.
The platform’s impact is already measurable. Siml reports more than 1,000 active stores and hundreds of thousands of agent actions executed on behalf of merchants in markets ranging from New Zealand to Mexico. Users see operational time savings of over three times compared with manual management. In the United States, Adobe Analytics noted a 393 % year‑over‑year surge in AI‑driven traffic to retail sites in the first quarter of 2026, with conversion rates flipping from 38 % below paid search to 42 % above by March 2026 and revenue per visit climbing 37 %.
Those numbers matter because the nature of online shopping is shifting. Buyers increasingly rely on virtual assistants that browse, compare and purchase without ever scrolling a product page. Yet Adobe found only 66 % of retail pages are machine‑readable, leaving a third of product information invisible to the agents driving the new traffic. McKinsey projects that agentic commerce could generate $3 to $5 trillion globally by 2030.
Siml also addresses a looming logistics headache. The United States suspended the de minimis customs exemption in 2025, a move confirmed in February 2026 that now forces roughly four million daily packages through formal customs treatment. Siml’s landed‑cost engine classifies products, computes duties and folds those costs directly into pricing, sparing merchants the spreadsheet nightmare that has become the norm for many sellers.
Neither founder claims the platform is a new storefront. Instead, Siml plugs into existing marketplaces, letting merchants keep their current sales channels while the AI runs the back‑office. "We had offers to join the companies people assume we’re competing with," Raimzhanov said, noting that Siml’s value lies in automating the work that still burdens sellers, not in replacing the platforms they already use.
Building an autonomous store required solving systems‑engineering challenges rather than merely training language models. The team had to create an entity‑resolution layer that could reconcile inconsistent product data across dozens of channels and develop a safety net that blocks any action lacking a confidence score above a human‑defined threshold. "If you cannot measure the accuracy of an action, you should not automate it," Abdullazhanov warned.
With backing from top Silicon Valley investors and a growing roster of global merchants, Siml is betting that the next wave of e‑commerce success will belong to sellers who let AI handle the day‑to‑day grind while they focus on product creation. The platform’s early results suggest that promise may soon become reality.
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