What used to take months, now takes hours,
thanks to AI and a smarter way to ask.
Sourcing Ally
Sourcing Ally
AI-powered supplier discovery built for clarity, speed, and confidence.
AI-powered supplier discovery built for clarity, speed, and confidence.
MY ROLE
Founding Product Designer
TIMELINE
3 months
NOTABLE CONTRIBUTIONS
Branding, End-to-End Experience, AI-Driven Innovation
MY ROLE
Founding Product Designer
TIMELINE
3 months
NOTABLE CONTRIBUTIONS
Branding, End-to-End Experience, AI-Driven Innovation
PROBLEM
Procurement teams struggle to find qualified, trustworthy suppliers.
Enterprises waste time vetting the wrong suppliers, leading to delays, higher costs, and risky sourcing decisions.
SOLUTION
Sourcing Ally: An AI-Powered, natural language prompt.
Users describe their sourcing needs in plain language and the system generates tailored supplier matches through AI-powered filtering, saving time, cutting costs, and surfacing only the best-fit suppliers.
PROBLEM
Procurement teams struggle to find qualified, trustworthy suppliers.
Enterprises waste time vetting the wrong suppliers, leading to delays, higher costs, and risky sourcing decisions.
SOLUTION
Sourcing Ally: An AI-Powered, sourcing assistant.
I designed a natural language prompt that allows users to describe their sourcing needs in plain language. Through AI-powered filtering, only the best-fit suppliers are presented, saving users time and money.
CONTEXT
What is Sourcing Ally?
Sourcing Ally is the product of Scaleup, a B2B that originated at AWS and went independent in 2023.
Learn more about the product history
What is Sourcing Ally?
Sourcing Ally is the product of Scaleup, a B2B that originated at AWS and went independent in 2023.
GALLERY
GALLERY
Product Challenges
SIMPLIFYING A COMPLEX PROCESS: We needed to turn a multi-step enterprise workflow (sourcing, vetting, validating) into a single prompt field.
DESIGNING FOR CONFIDENCE: Enterprise users needed to trust the AI, the matches, and the data behind them, without feeling overwhelmed.
DESIGNING AROUND AI (NOT FOR IT): We were designing a product around AI logic, which meant working with uncertainty, edge cases, and natural language interpretation.
INVENTING THE WHEEL: There weren’t templates or “best practices” for what we were building. We had to invent the experience as we went along, making thoughtful decisions quickly and without a roadmap.
Project Constraints
3 MONTH TIMELINE
We had just 3 months to ideate, design, and ship the product - a tight window that demanded focus, fast decision-making, and a clear strategy from day one.
LOW FUNDING
Funding was tight by the time we landed on Sourcing Ally as an idea. We had limited resources and an accelerated timeline to design and ship.
SMALL TEAM, BIG WORKLOAD
Everyone wore multiple hats. As the sole designer, I balanced UI, UX, research, and systems thinking, while collaborating closely with leadership, product, and engineering.
Getting to the root of the problem…
Through dozens of user interviews and industry research, 3 core pain points consistently emerged:
Learn more about the product history
So, what was our goal?
PROJECT GOALS
To simplify the sourcing process - start with a natural language prompt and speed up supplier discovery by letting AI do the heavy lifting.
Project Constraints
3 MONTH TIMELINE
We had just 3 months to ideate, design, and ship the product - a tight window that demanded focus, fast decision-making, and a clear strategy from day one.
LOW FUNDING
Funding was tight by the time we landed on Sourcing Ally as an idea. We had limited resources and an accelerated timeline to design and ship.
SMALL TEAM, BIG WORKLOAD
Everyone wore multiple hats. As the sole designer, I balanced UI, UX, research, and systems thinking, while collaborating closely with leadership, product, and engineering.
CASE STUDY | 7 min read
RESEARCH
Getting to the root of the problem…
Through dozens of user interviews and industry research, 3 core pain points consistently emerged:
SOURCING SUPPLIERS TAKES TOO LONG: On average, it takes 57 days just to go from posting a request to finding a supplier.
GOOD SUPPLIERS ARE HARD TO FIND: Procurement teams struggle to find credible, compatible suppliers, despite having access to numerous supplier databases.
COSTLY SETBACKS: 93% of supply chain leaders say bad supplier data, like outdated contact information or missing info, leads to costly delays and wasted time.
COMPETITIVE ANALYSIS
Gaps in Relevance, Transparency, and Usability
Procurement teams don’t just need access to suppliers, they need relevant, qualified ones they can trust. I analyzed platforms like Supplier.io, TealBook, and Thomasnet through the lens of those I interviewed, and identified consistent UX gaps: strict search tools, too many results to sort through, and no clear explanation of why or how results are generated, leaving users to do all the work.
As a result, I focused on making the sourcing experience more efficient, guided, personalized, and intentional.
SUPPLIER.IO
Large supplier database
Trusted by Fortune 1000 companies
Integrates well with enterprise procurement workflows
Focuses heavily on certifications and not capability
Primarily a search directory
Rigid UI and form-based filters
No real-time guidance
PROS
CONS
TEALBOOK
Real-time supplier data enrichment using AI
Promotes data visibility across procurement systems
Less focus on matchmaking for specific sourcing requests
Can be data-heavy but not sourcing-decision friendly
Less intuitive UX for new sourcing professionals
PROS
CONS
THOMASNET
Huge volume of suppliers, especially in manufacturing
Has category-based filtering
Outdated user experience - clunky and dated UI
No vetting options or compatibility scoring
Lacks user guidance
PROS
CONS
RESEARCH
PROJECT GOALS
So, what was our goal?
To simplify the sourcing process - start with a natural language prompt and speed up supplier discovery by letting AI do the heavy lifting.
COMPETITIVE ANALYSIS
Gaps in Relevance, Transparency, and Usability
Procurement teams don’t just need access to suppliers, they need relevant, qualified ones they can trust. I analyzed platforms like Supplier.io, TealBook, and Thomasnet through the lens of those I interviewed, and identified consistent UX gaps: strict search tools, too many results to sort through, and no clear explanation of why or how results are generated, leaving users to do all the work.
As a result, I chose to prioritize a natural language input flow, AI-enhanced refinement, and transparent match criteria, so users feel supported at every step of the sourcing journey.
SUPPLIER.IO
Large supplier database
Trusted by Fortune 1000 companies
Integrates well with enterprise procurement workflows
Focuses heavily on certifications and not capability
Primarily a search directory
Rigid UI and form-based filters
No real-time guidance
PROS
CONS
TEALBOOK
Real-time supplier data enrichment using AI
Promotes data visibility across procurement systems
Less focus on matchmaking for specific sourcing requests
Can be data-heavy but not sourcing-decision friendly
Less intuitive UX for new sourcing professionals
PROS
CONS
THOMASNET
Huge volume of suppliers, especially in manufacturing
Has category-based filtering
Outdated user experience - clunky and dated UI
No vetting options or compatibility scoring
Lacks user guidance
PROS
CONS
With a tight timeline ahead, we went straight into designing the final UI, testing early and often.
UI LIBRARY
Establishing a Visual Tone
I designed the visual tone around the product’s core values: trust, clarity, and focus. Blue was chosen for its associations with calm and reliability. A simple, monotone palette reduced noise and highlighted key actions, keeping the UI clean and appropriate for an enterprise environment.
VISUAL DESIGN
Supplier Sourcing Meets AI
Since this was an end-to-end journey, we had to think through everything - from submitting a request to making first contact with a supplier. This meant designing a flow that felt intuitive from the start, guided by AI when needed, and transparent enough to help users feel confident in their decisions.
SOLUTION #1
NATURAL LANGUAGE PROMPT
Users describe what they need in plain language. An AI enhancement feature allows users to refine their request, removing friction while improving accuracy.
View Details
SOLUTION #2
CURATED MATCHES
The system extracts key requirements and generates tailored matches, helping users focus only on options that meet their specific needs and standards.
View Details
SOLUTION #3
SUPPLIER PROFILES
Structured, scannable profiles highlight what matters most. Users have the transparency and confidence they need to make informed decisions.
View Details
USER TESTING
USER TESTING
Integrating User Feedback
Integrating User Feedback
We listened closely to what mattered most - what information users needed, what could help them feel confident in making a decision, and what could potentially get in their way.
As we began to build the product, we tested early and often, incorporating feedback along the way to ensure every interaction was building on the next, helping users move through the sourcing process confidently.
We listened closely to what mattered most - what information users needed, what could help them feel confident in making a decision, and what could potentially get in their way.
As we began to build the product, we tested early and often, incorporating feedback along the way to ensure every interaction was building on the next, helping users move through the sourcing process confidently.
PRODUCT UPDATE #1
BRIDGING MENTAL GAPS
Improving visibility and clarity between extracted requirements and match results.
View Details
PRODUCT UPDATE #2
TAILORING TO OUR USERS
Personalizing the match experience by providing users with additional vetting options.
View Details
PRODUCT UPDATE #3
SHOWING PRODUCT VALUE
Improving UX by giving users a sneak-peek of the benefits of upgrading their subscription.
View Details
PRODUCT UPDATE #1
BRIDGING MENTAL GAPS
Improving visibility and clarity between extracted requirements and match results.
View Details
PRODUCT UPDATE #2
TAILORING TO OUR USERS
Personalizing the match experience by providing users with additional vetting options.
View Details
PRODUCT UPDATE #3
SHOWING PRODUCT VALUE
Improving UX by giving users a sneak-peek of the benefits of upgrading their subscription.
View Details
SOURCING SUPPLIERS TAKES TOO LONG: On average, it takes 57 days just to go from posting a request to finding a supplier.
GOOD SUPPLIERS ARE HARD TO FIND: Procurement teams struggle to find credible, compatible suppliers, despite having access to numerous supplier databases,
COSTLY SETBACKS: 93% of supply chain leaders say bad supplier data, like outdated contact information or missing info, leads to costly delays and wasted time.
VISUAL DESIGN
Supplier Sourcing Meets AI
Since this was an end-to-end journey, we had to think through everything - from submitting a request to making first contact with a supplier. This meant designing a flow that felt intuitive from the start, guided by AI when needed, and transparent enough to help users feel confident in their decisions.
SOLUTION #1
NATURAL LANGUAGE PROMPT
Users describe what they need in plain language. An AI enhancement feature allows users to refine their request, removing friction while improving accuracy.
View Details
SOLUTION #2
CURATED MATCHES
The system extracts key requirements and generates tailored matches, helping users focus only on options that meet their specific needs and standards.
View Details
SOLUTION #3
SUPPLIER PROFILES
Structured, scannable profiles highlight what matters most. Users have the transparency and confidence they need to make informed decisions.
View Details
With a tight timeline ahead, we went straight into designing the final UI, testing early and often.
UI LIBRARY
Establishing a Visual Tone
I designed the visual tone around the product’s core values: trust, clarity, and focus. Blue was chosen for its associations with calm and reliability. A simple, monotone palette reduced noise and highlighted key actions, keeping the UI clean and appropriate for an enterprise environment.
Product Challenges
SIMPLIFYING A COMPLEX PROCESS: We needed to turn a multi-step enterprise workflow (sourcing, vetting, validating) into a single prompt field.
DESIGNING FOR CONFIDENCE: Enterprise users needed to trust the AI, the matches, and the data behind them, without feeling overwhelmed.
DESIGNING AROUND AI (NOT FOR IT): We were designing a product around AI logic, which meant working with uncertainty, edge cases, and natural language interpretation.
INVENTING THE WHEEL: There weren’t templates or “best practices” for what we were building. We had to invent the experience as we went along, making thoughtful decisions quickly and without a roadmap.