2024-ongoing
Industrial Web Search
From precision search to AI-assisted industrial sourcing.
Industrial Web Search is a B2B sourcing platform designed to help industrial buyers find suppliers based on actual capabilities rather than paid placement. I helped shape IWS from its original search and marketplace experience through its evolution into an AI-assisted platform. My work spanned product strategy, UX/UI design, research, information architecture, prototyping, design systems, developer handoff, QA, and continued product iteration.
THE OPPORTUNITY
Industrial search had a relevance problem.
Industrial buyers often know exactly what they need technically, but finding the right supplier can still be difficult. Traditional directories prioritize visibility, broad categories, or paid placement, while terminology can vary significantly between buyers and suppliers.
The opportunity behind IWS was to create a more precise sourcing experience: one where suppliers were surfaced because their capabilities matched the buyer's needs, not because they paid to appear first.
THE FOUNDATION
Better matching starts with better information.
Before improving search, we needed to create structure around how suppliers represented themselves.
I helped design a system that allowed suppliers to build detailed profiles around their capabilities, products, industries, keywords, media, and company information. That structure gave buyers more meaningful information to search against while creating a stronger foundation for matching behind the scenes.
This became especially important later. The more structured and accurate the supplier information became, the more useful IWS could be when interpreting buyer intent with AI.
TWO SIDES OF THE MARKETPLACE
One platform, two very different jobs to be done.
Buyers and suppliers enter IWS with fundamentally different goals.
Buyers need to move quickly from a requirement to a shortlist of credible suppliers. Suppliers need visibility, control over how their capabilities are represented, and insight into how buyers are discovering them.
Rather than forcing both groups into the same experience, I designed separate workflows around what each side needed to accomplish while keeping them connected through the same underlying marketplace.
BUYER EXPERIENCE
Search needed to support how sourcing actually happens.
Industrial sourcing rarely ends with a single search.
Buyers compare suppliers, revisit options, save companies, make notes, and eventually reach out for more information. I designed the buyer experience around that broader workflow, connecting search with supplier profiles, favorites, notes, and requests for information.
The goal was to reduce the amount of work happening outside the platform and make IWS useful throughout the sourcing process, not just at the moment of discovery.
EVOLVING THE PRODUCT
The next question wasn't where AI could fit. It was where AI could actually help.
The original IWS experience depended heavily on structured search and matching. That worked well when buyers knew the terminology used by suppliers, but industrial requirements are often much more nuanced than a single keyword.
As the product matured, we explored how AI could reduce that dependency without replacing the precision and structure already built into the platform.
The opportunity was to let buyers describe what they needed more naturally while using AI to interpret those requirements and connect them with relevant supplier capabilities.
AI FOR BUYERS
Search became less about entering the right words and more about expressing the right need.
With AI-assisted search, buyers can describe their requirements in more natural language instead of having to know the exact terminology used within the marketplace.
The system can interpret those requirements, consider technical specifications and supplier capabilities, and help surface companies that align more closely with what the buyer is actually looking for.
For me, the important design challenge was maintaining trust and clarity. AI needed to make discovery easier without turning the experience into a black box or removing the buyer's ability to evaluate suppliers themselves.
AI FOR SUPPLIERS
Improving the other side of the match.
Better buyer intent only solves half of the problem. AI matching is also dependent on the quality of the information suppliers provide.
I partnered with the team to design an AI chat experience inside the supplier portal that helps users further develop their presence on IWS.
Instead of expecting suppliers to understand every part of the platform on their own, the assistant creates a more conversational way to identify opportunities to strengthen their information and better represent what they can provide.
The goal wasn't to take control away from suppliers. It was to reduce the effort required to build a stronger, more complete presence.
THE SYSTEM
Stronger information on both sides creates better matches in the middle.
The evolution of IWS made one thing increasingly clear: the buyer and supplier AI experiences shouldn't be thought of as separate features.
Buyers are being helped to better communicate what they need.
Suppliers are being helped to better communicate what they provide.
IWS sits between those two sides, using that information to create more relevant connections.
That systems-level relationship became an important part of how I approached the AI experience. Improving one side of the marketplace directly strengthens the experience on the other.
PRODUCT EVOLUTION
From a search concept to a live, evolving platform.
IWS began as an effort to rethink how industrial buyers and suppliers find one another.
What started with structured search, supplier profiles, and two-sided marketplace workflows has continued to evolve into a platform that uses AI to better understand both buyer needs and supplier capabilities.
Working on IWS over time has meant designing beyond an initial launch. I've had to revisit earlier assumptions, adapt the experience as the product matured, and think about how new technology fits into an existing system without losing what made that system useful in the first place.
REFLECTION
The strongest AI experience started long before we added AI.
One of my biggest takeaways from IWS is that intelligent experiences still depend on thoughtful product foundations.
The supplier information architecture, capability structure, search logic, and marketplace workflows we designed early on became increasingly valuable as the platform evolved.
AI changed how users could interact with that information, but the quality of the experience still depended on the structure underneath it.
That shift changed how I think about designing AI products: the interface may become more conversational, but the systems, information, and user needs behind it matter more than ever.
