Year
Feb'26 – Mar'26
8 weeks
Tools
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Scope
Research · Product strategy · IA · Enterprise UX · AI vision
Role
UX Researcher
Product Designer
Reimagining industrial commerce
Designing an adaptive B2B commerce experience for complex tooling decisions
InTech sells high-precision tooling systems used across aerospace, automotive, medical, defense and other manufacturing industries. Its business was built primarily through distributors, who generated approximately 95% of revenue, while leadership wanted to expand self-service commerce and explore AI-assisted product discovery.
But the problem wasn't simply an outdated website.
Different buyers entered the purchasing journey with fundamentally different levels of knowledge, yet both eventually depended on human intervention to complete the purchase.
Working alongside a Senior UX Researcher, I conducted and translated research into the product strategy, information architecture and future-state experience for a industrial commerce platform.
The Challenge
Industrial buyers could discover products online, but the journey often broke down when they needed to validate fit, pricing, availability, customization or place an order. Expert buyers wanted speed and transaction confidence, while occasional buyers needed help identifying the right tool and parts. Meanwhile, leadership wanted greater self-service and direct digital commerce without disrupting a distributor network responsible for ~95% of revenue.
User Need
Help me find the right tooling, confirm it will work and confidently move forward without relying on a sales call.
Business Need
Grow digital commerce and self-service without bypassing trusted distributors.
How might we help buyers independently discover, validate and move complex tooling purchases forward while protecting the distributor relationships the business depends on?
Understanding how industrial buying actually works
We spoke with stakeholders and buyers across the ecosystem, audited the existing purchasing journey and benchmarked industrial commerce experiences to understand where digital self-service broke down and and human intervention began.
8 Stakeholder Interviews
(leadership, sales, operations, customer support )
13 User Interviews
(distributors, technical consultants, engineers, small manufacturers)
End to end
Platform Audit
Competitive
Benchmarking
Four insights shaped the product
01. The system has most of the pieces, but they are fragmented.
Machine compatibility, CAD, specifications, product relationships and saved items already existed, but across disconnected workflows. Buyers still needed people to connect the dots.
02. Experts and occasional buyers needed opposite experiences
Experts move quickly using specifications and part numbers. Less-frequent buyers need guidance from machine and application to a compatible solution. One rigid, broken workflow frustrated both.
03. Discovery stopped too early
Products could be found online, but pricing, inventory, customization and ordering often required a sales representative.
04. Digital growth couldn't mean distributor displacement
Buyers want less friction, and so does the business. But with ~95% of sales coming through distributors, any shift toward direct commerce had to avoid channel conflict, distributor losses and near-term revenue risk.
Two buying modes.
Power User
Distributors · Technical consultants · Experienced engineers · Sales Team
Knows: Machine terminology, specifications, interfaces, often the part or product family.
Needs: Speed, technical depth, CAD, quotations
Current behavior:
Search → Add to cart → Call to get details & order.
Light User
Local Dealers · Hobbyists · Engineering Students
Knows: the goal ( build / get something done) but not necessarily the correct tools/ parts needed.
Needs: Guidance, compatibility and confidence.
Current behavior:
Browse → get overwhelmed → call specialist.
Product Strategy
Build for today, while creating the foundation for tomorrow
A fully autonomous commerce experience was not realistic in the short term. Pricing, compatibility, inventory and ordering still depended heavily on sales and technical support, while the data needed for reliable AI-assisted recommendations was fragmented.
So we proposed a phased strategy:
Phase 1
Connect
Unify discovery, configuration, validation, commerce and specialist support into one coherent journey.
Prioritized Features
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Adaptive Discovery
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Specialist Handoff
Phase 2
Assist
Move from a product search to a sophisticated console for intuitive configuration and technical validation.
Potential Features
Sophisticated Console for intuitive configuration and technical validation.
Phase 3
Orchestrate
Introduce an Engineering Copilot that can find, configure, validate and move purchases forward conversationally.
Potential Features
AI chat for product discovery, configurations, getting quotes and placing orders.
The goal is not to remove human expertise but to progressively reduce the reasons buyers needed to call.
Proposed User Journey
Design Principle applied
Meet users where their expertise begins
Give experienced buyers a direct path to what they know, while offering guidance to buyers who need help defining the right solution.
Create digital convenience without breaking channel trust
Increase buyer self-service while preserving the distributor relationships the business depends on, particularly during the transition toward direct commerce.
Automate certainty, not relationships
Use the system to handle repeatable validation and routine decisions, while keeping specialists accessible when the stakes, ambiguity, or complexity increase.
Turn recommendations into confidence
Not just tell buyers what fits. Show why it fits through compatibility checks, specifications, drawings, and clear technical evidence.
Proposed User Journey
Everyone is trying to get from a need → to a technically valid configuration → to a commercial decision.
What changes is how much of the journey they need help with.
Adaptive Discovery
Multiple Paths to Product Discovery
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Design Rationale
The original experience expected buyers to understand the product catalog before they could find the right path. I reorganized the entry points around what buyers already know: search directly when they know the product, use guided discovery when they need help, and contact a specialist when the system is not enough.
Flexibility & Efficiency of Use · Progressive Disclosure · Recognition over Recall
Tradeoff
Adding multiple discovery paths improves flexibility, but introduces more choice upfront. I limited the entry points to distinct buyer intents and moved detailed filters and catalog complexity downstream.
Product Filtering
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Design Rationale
The existing Tool Finder made every buyer go through the same sequence of steps, even when they already knew what they were looking for. I replaced it with a filter-based experience that lets buyers start with the information they already have and narrow results from there.
Flexibility & Efficiency of Use · Recognition Rather Than Recall · Match Between System & Real World
Tradeoff
Showing filters, product details, and compatibility information on one screen increases information density. I accepted that tradeoff to reduce unnecessary steps and make the experience faster for experienced buyers, while keeping the information structured and scannable for less-familiar buyers.
Contextual Specialist Collaboration
Design Rationale
Some tooling decisions still require technical expertise, so removing human support in Phase 1 would create risk. Instead, I redesigned support as a contextual handoff: specialists inherit the buyer's current search and configuration, then use screen sharing to resolve uncertainty and validate choices without restarting the journey.
Human in Loop · Recognition Rather Than Recall · User control and freedom
Tradeoff
Live collaboration retains operational dependency on specialists rather than fully automating support. I accepted that dependency in
Phase 1 to reduce purchase risk while using structured handoffs and recommendations to make each interaction faster and create data for future assistance.