Brian C. Reyes
WireScreen Pathfinder — network graphing tool for entity-relationship discovery

Addressing the question that was stalling sales — before anyone asked.

The WireScreen data was valuable, but finding hidden relationships — a critical task for government analysts — had a serious bottleneck. Tracing corporate networks required manually clicking through profiles, extracting data manually from tables, and piecing relationships together by hand in external tools. One of the most common asks in sales demos was “can the platform show me the connections between Company A and Company B?” The answer was no, and that no was slowing sales cycles and costing us deals.

Project Details

COMPANY
WireScreen
ROLE
Lead UX Designer
LENGTH
4 months
TEAM
1 UX + 5 dev + 2 PM
YEAR
2025

About WireScreen

WireScreen is a corporate intelligence platform founded by two-time Pulitzer Prize winner David Barboza. Clients pay six to seven figures for access, because it allows government analysts to uncover hidden relationships between companies that cause regulatory risk or national security threats.

David recruited me specifically because I’d designed PitchBook. He wanted that same rigor applied to making a world-class dataset actually usable.

The problem

The gap wasn’t in the data. It was in the tool’s ability to justify the price tag

The problem

WireScreen had incredibly valuable data, just no easy way to see how it linked companies together.

The cost

WireScreen sells risk intelligence at enterprise prices. Without a connection layer, analysts struggled to show check writers the value.

The stakes

Seven-figure deals stalled. Sales cycles dragged. Renewals got harder to defend.

Company A and Company B with unknown connections between them

The platform couldn’t find the connection between two companies — analysts had to do it completely manually.

The problem

This is how analysts used “world-class data” to answer

“Is Company ‘A’ connected to Company ‘B’?”

  1. 01Open Company A’s profile
  2. 02Investigate each of company A’s investments
  3. 03Search through the investments of the investments
  4. 04Search through the investments of the investments of the investments
  5. 05Investigate companies with shared address, phone, website, email
  6. 06Investigate executives looking for connections between both companies
  7. 07Repeat steps 1–6 for shareholders…
  8. 08Repeat steps 1–6 for supply chain customers…
  9. 09Repeat steps 1–6 for suppliers…
  10. 10Repeat steps 1–6 for controlled companies without direct ownership…
The data was useful to analysts.The workflow was not.
How do you build withno requirements set in stone?
Decision 1.Make connection searching the north star

Searching for connections between companies wasn’t on the roadmap yet, but graphing company networks was. I made graphing the Phase 1 and connection searching became Phase 2. Each Phase 1 decision would build towards Phase 2: the graphing platform, rendering model, and feature sequencing. Phase 1 would build the momentum for Phase 2, and when Phase 2 was green-lit, we would have the foundation.

Phase 1: Graphing leading to Phase 2: Connection Search
Decision 2.I advocated for the more powerful graphing library.

The team was evaluating lightweight graphing libraries. I pushed for ReGraph — a more powerful, enterprise-grade library — because of what it could do at scale. It came with a rich component model, flexible APIs, and rendering capabilities that would support the complex networks Pathfinder would eventually need to reveal.

ReGraph enterprise graphing library component examples
Decision 3.I reduced graph noise with smart logic

Entity networks are dense. A single company can have dozens of shareholders, investments, subsidiaries, and officers. Rendering everything at once creates noise, not insight. I designed a rendering model that flattened bi-directional relationships — cutting down on noise while still enabling filtering. Users didn’t have to think — it just worked. It helped the platform scale and visualize complex networks without overwhelming.

Bi-directional relationship flattening reduces graph noise

Simplifying how relationships are rendered on the graph helped display complex networks without adding extra noise.

Decision 4.I determined what had to ship with v1.

I prioritized what was necessary to ship value. My read was that an unfiltered graph would open to visual noise: hundreds of nodes, no way to orient, no clear entry point. Analysts would open it once, decide it didn’t work, and never come back. A bad first impression on a feature this visible would be harder to recover from than a delayed launch, and a failed Phase 1 meant Pathfinder never gets approved. Filters shipped with v1. The graph was usable on day one.

Pathfinder v1.0 — core graph with filters and canvas controls
Decision 5.Sequencing features.

Once analysts were exploring graphs the next problem became obvious. They needed to show the network graphs to someone; their coworkers, a procurement committee, a decision-maker. Export as SVG was the next feature — high resolution, small file size, designed for transport to the boardroom presentation. Download unlocked the external use case.

As analysts began exploring and creating more graphs — filtering, expanding, repositioning company nodes — they were investing real effort. Losing that work on logout wasn’t a minor inconvenience. It was a reason not to use the feature at all. The Save feature came next.

Pathfinder v1.1 — Export and custom controls addedPathfinder v1.2 — Open/Save added, download folded into menu

We didn’t just change usage metrics.
We changed sales conversations.

Pathfinder connection search result displayed on a monitor

Results.

The feature, Pathfinder, shipped. The answer to “Can I connect Company A to Company B?” became a yes. A research process that previously took days of manual traversal now runs in minutes. Pathfinder became a deal-closer. In a product sold at six to seven figures where every contract requires justifying cost to a check-writer, a feature that answers the question killing demos has a direct line to revenue.

7-figure deal closed

Sales attributed the platform’s new features as being the difference maker in closing the largest deal in company history.

Boardroom ready

High-resolution network exports drop straight into the report, directly impacting decision-making.

Research speed

What took days of manual traversal and research effort now runs in minutes.

Customer feedback validating Pathfinder

User feedback directly validated the work.