Automating quality control

Reducing document review from days to minutes, and helping GlobalVision reach $1M ARR in year one

Verify is a document quality control platform built from scratch for pharmaceutical teams. I was the founding product designer.

The Constraint

Verify is a document quality control platform designed for pharmaceutical teams operating in highly regulated environments.

Before Verify, document reviews relied on manual proofreading across multiple departments, introducing delays, risk, and inconsistent outcomes. As review cycles scaled, release timelines stretched from hours into days, creating operational bottlenecks and compliance pressure.

The problem was not accuracy.
It was speed, trust, and scale.

Ownership

I was the founding product designer responsible for defining how document inspection would be automated in a regulated environment.

This included shaping the inspection workflow, determining where automation could safely replace manual review, and validating that results met the trust requirements of compliance and regulatory teams.

The goal was not to speed up proofreading.
The goal was to reduce release risk while maintaining regulatory confidence.

The problem

Pharmaceutical documents move through multiple review stages involving Regulatory Affairs, Compliance, and external authorities.

Manual proofreading made this process slow and fragile. Each revision increased review time, introduced human error, and delayed release. As document volume increased, teams could not scale quality control without adding headcount or accepting risk.

This was not a tooling gap.
It was a process constraint.

Research and Discovery

Verify is the newest addition to GlobalVision’s suite of document quality control software. This application addresses a particular problem in the upstream space, which spans the creation of the initial Word document to the final, print-ready file.

What We Found

We spoke directly with pharmaceutical regulatory and compliance teams to understand how document review actually worked in practice. Three findings shaped the design:

  • Reviewers were comparing documents side-by-side manually, sometimes for hours. The bottleneck wasn't attention · it was tooling. There was no way to surface differences automatically.
  • Compliance teams needed to trust the output before acting on it. Any automated tool would need to show its work, not just flag differences, but categorize them so reviewers could prioritize.
  • "Just make the software do it for me" came up repeatedly. Reviewers didn't want to manually match graphics · they wanted the platform to handle matching entirely.

These three findings · automation of comparison, explainability of results, and the need to remove manual graphic matching · became the pillars of the Verify design.

Design Process

We ran parallel tracks: defining the inspection workflow while validating prototypes with compliance teams at each stage. Key phases:

  • Implementing the double diamond methodology to define user problems
  • Multiple iterations of wireframes
  • Creating Figma prototypes
  • Developing mini Proof of Concepts (POCs)
  • Validating every feature with our end users

Challenges

Manual Graphic Inspections

Inspecting graphic elements is a significant use case for our customers.

  • Documents up for review come in various shapes and sizes and are not always 1:1, presenting a challenge in facilitating the selection and matching process.
  • Our first iteration of the graphics feature fell short.
  • We provided a manual tool that, depending on the user’s computer literacy level, became a major source of frustration.

Automated Graphic Inspections

Given the substantial feedback we received suggesting “just make the software do it for me”, we realized we needed to pivot towards a solution that would automate the matching process and directly provide our users with the results.

The Solution

Our team has taken several steps to enhance the user experience and efficiency of our application:

  • The application was designed with a linear structure, guiding users through each inspection stage.
  • We initiated a Prep phase, asking users about their inspection priorities. This made results review easier, as we only highlighted relevant errors.
  • To streamline the review of results, we’ve implemented a tagging system for the identified errors. This system, referred to as “Difference Type”, provides additional context to help users expedite their review times.

Results
  • Shipped a production version within 8 months.
  • Document review time: reduced from days to under 10 minutes on average.
  • GlobalVision reached $1M ARR in year one · Verify was a key part of that growth.

Conclusion

Verify reduced pharmaceutical document review from days to under 10 minutes, and contributed to GlobalVision reaching $1M ARR in its first year. The most important design decision wasn't the automation itself · it was making the output trustworthy enough for compliance teams to act on. We learned that early when our first graphics inspection feature failed: we built a manual selection tool that frustrated anyone without high computer literacy. User feedback was direct · "just make the software do it." We rebuilt it as a fully automated matching system. That pivot, and the willingness to scrap a shipped feature, is what made the product credible in a regulated environment.