From Traditional Craft to Technology: How a Carpet Maker from Uzbekistan Launched an AI Startup

Hikmatillo Umarhojiyev, City — San Francisco, CEO, Linkedin

How It All Started

I am a technology entrepreneur and a master of handmade carpet weaving. I began my career in traditional industries — carpet weaving and textile trading. For over 15 years, I worked in craft production, retail operations, and customer engagement, growing a business in an industry that had changed very little for decades.

Over time, I realized that the industry’s core problems lay not in the artistic or product aspects, but in its structure. Expensive carpets were sold using outdated models; buyers lacked confidence before visiting a store, and consultants spent vast amounts of time on repetitive, low-impact interactions. This hindered growth, inefficiently utilized human expertise, and prevented the scaling of traditional businesses.

I consciously decided not to leave the industry but to transition into technology. My goal was to understand how artificial intelligence could be applied responsibly to solve long-standing problems in traditional business. I studied modern AI systems not to replace craftsmanship, but to turn advanced technologies into practical tools accessible to retail sellers.

This journey — from traditional entrepreneurship to technology and back to a heritage industry — became the foundation of my work. Combining deep industry expertise with artificial intelligence and working alongside my co-founder Shavkat Khomidov, who handles technical architecture and systems engineering, we created the Findecor platform. It helps understand the customer’s space, preferences, and intentions even before they visit a store, changing the very process of choosing and buying carpets while preserving the value of human expertise.

Startup Launch

The company was born not from theory, but through direct validation within the industry. I developed an early AI prototype and tested it with real carpet sellers using live inventory data. In the initial stage, I personally handled the product concept, client acquisition, and pilot demonstrations, while Shavkat Khomidov was responsible for system architecture, AI infrastructure, and technical implementation.

We consciously avoided chasing rapid scaling, instead growing through sequential iterations alongside physical stores in the US. The team formed gradually: first AI engineers and architects, then product and business specialists as the platform matured. This approach allowed us to address fundamental industry problems before expanding functionality.

Target Audience

The startup is focused on carpet and rug sellers, particularly mid-sized and large stores with extensive assortments and sales teams.

The platform enables:

— analyzing rooms and customer preferences before they visit the store
— recommending relevant products based on actual inventory and sales priorities
— reducing repetitive consultations
— increasing conversion rates while preserving the value of human expertise.

Today, the platform has been tested with several stores in the San Francisco Bay Area — in Menlo Park, Palo Alto, and San Francisco. Currently, we are in negotiations with approximately 30 new partners and plan to reach 300 active clients by the end of 2026.

Incubation Programs

The startup was selected and supported by several international innovation programs. It is part of the Silkroad Innovation Hub and the AlchemistX Silicon Valley Residency, which focus on globally scalable technology companies.

Additionally, we collaborate with Astana Hub and IT Park Uzbekistan, reflecting our role at the intersection of technological innovation and traditional industries across different regions. These programs played a key role in validating the product’s relevance in the US market and positioning it as a cross-border solution.

Challenges

One of the main challenges was implementing artificial intelligence in a deeply traditional industry. Many sellers initially viewed AI as a threat to human expertise. In our pilots, we demonstrated that AI works best as a support tool — assisting consultants rather than replacing them.

Another challenge was the lack of structured digital data in the carpet industry. Product information was often fragmented and inconsistent. To solve this, we developed systems for data normalization, matching, and recommendations, which became a key technical differentiator of the platform.

The key lesson: innovation in traditional industries requires trust, patience, and deep knowledge, not just technology.

Achievements

We have created a specialized AI platform aimed at solving long-standing structural problems in carpet retail. The project successfully completed pilots in several US stores and was selected for prestigious international acceleration programs. During this time, we have built a growing pool of partner retailers in the US market, and the product has practically demonstrated how artificial intelligence can modernize industries with rich heritage while preserving the value of human experience and expertise.

Plans

By the end of the year, we will focus on expanding our presence in the US, converting pilot projects into long-term partnerships, and improving the accuracy of AI-based room analysis and recommendations.

In the long term, our goal is to scale the platform across the entire US and enter new global markets where traditional retail faces similar structural challenges. Our mission is to show that artificial intelligence can responsibly modernize heritage industries without destroying the craftsmanship, knowledge, and human judgment that define them.