One home base for every RFQ

Vendors are grouped by status: quoted, no-bid and still waiting. The next action sits on each row, so following up or rejecting takes one click.

Awarding used to mean digging through email threads. Now AI quote leveling compares every bid, recommends one, and leads straight into a PO email pre-filled with the quoted line items.

Seeing where the money goes

With quotes as structured data, buyers see spend by category, each row linked to its RFQ.

AI pulls shipping terms and line items out of each PO and tracks delivery for every item.

Rule: buyers never retype what a vendor already sent.

Deciding what to build, together

For AI filtering, users pick a filter or describe the RFQs they need in plain language, and AI builds the filter. I defined the behavior and settled the hard questions with engineering early: what gets saved, how results rank, and what is searchable.

Then the whole team wrote the filters they'd actually type. Those stickies became the first set of supported queries.

Suggest, show your work, then wait

Vendor emails are full of updates: a new contact, a new phone number, a moved warehouse. The agent reads each thread and proposes changes, keeping vendor records accurate without manual work.

Every change is a suggestion the buyer approves or rejects, shown next to the source email and the old value it replaces.

The AI never edits a record on its own.

Quote leveling and chat-based RFQs

AI quote leveling compares every quote in an RFQ and recommends one. Session reviews show which comparisons buyers need, so we keep adding them.

Chat-based RFQ creation turns existing PDFs and spreadsheets into RFQs by asking buyers a few follow-up questions.

Advertising without a sales call

Suppliers could only buy advertising through a sales rep. We needed a self-serve channel alongside sales.

I ran a 4-day design sprint and tested the prototype with 5 customers before any code was written.

+25% new customers. Checkout takes about 5 minutes.

Three steps: keywords, budget, checkout

Suppliers pick keywords, choose a recommended budget, and check out. I worked with data analysts so recommended budgets deliver the most search activity per dollar.

My cross-functional service blueprint workshops aligned executives, sales, finance and support before launch.

Shipping, then improving

Oct 2024: a proposal tool helped sales reps follow up on abandoned checkouts, closing 50 new sales in two months.

Dec 2024: customers can edit live campaigns, which opened upsells and raised average spend 15%.

Jan 2025: annual payment plans, designed with finance and sales, for predictable revenue.

ROI reports in 4 minutes, not an hour

A data platform migration forced a rebuild of sales reporting. I surveyed the sales team with our UX researcher to find their core reports and pain points.

I designed configurable tables, a shared report library, client-ready exports and ROI charts. The entire sales team adopted it.

Eight tools down to one

The content team juggled 8 separate apps and tracked changes in CSV files. My workflow workshops and process maps shaped both the UX and the technical architecture.

Over 2+ years we shipped one platform with a full change log, and moved the entire content team onto it.

One identity across product and marketing

Guidelines set the logo, color and type so decks, the website and the product look like one company.

Product moments, simplified

Simplified product moments replace dense screenshots, so each feature reads at a glance.

The brand, carried into the product

A short sign-up form carries the brand into the product and steers people to work email, since Purchaser runs on the company inbox.