How Florence Healthcare Used Arovy to Bring More Context to Salesforce

After inheriting a Salesforce org with limited documentation, overlapping automations, and a lot of unanswered questions, Florence Healthcare used Arovy to better understand how fields and automations were connected, reduce tech debt, support onboarding, and improve visibility across the org.

The Challenge: A Black Box

At Florence Healthcare, one of the biggest challenges was making sense of an inherited Salesforce org that did not have strong documentation. Important context was missing, naming was not always clear, and some parts of the system were difficult to interpret without additional tooling.

In describing the environment, Ashley Houser referred to Florence’s Salesforce instance as a “black box” and said some areas felt like “literal black holes” without a way to understand what those things were for.

That lack of context became especially difficult when trying to understand why certain fields existed, how they were being used, and what else they touched across the org.

Untangling Automation and Reducing Tech Debt

Automation was one of the clearest examples of that complexity. When Florence began working through the org, it had more than 400 automations in place. Many of them were stacking on top of each other, creating dependencies where they should not have, or triggering other processes in ways that made troubleshooting harder.

Arovy gave the team a way to see how fields interacted with automations, reports, validation rules, and workflow rules. That visibility made it easier to understand how things were interconnected and why something was in the org in the first place.

It also helped with tech debt. One example was low-usage fields. Because Arovy shows what percentage of a field is populated, Florence could use that as a reference point to identify fields with very low usage and flag some of them as candidates for deletion. That helped reduce noise for end users and clean up fields that may have been created with good intentions but were never really used.

“It saves hours of everyone on our team.” 

“We could just point them directly to Arovy, and they could find it themselves if they wanted to.”

Faster Onboarding and Easier Self-Serve Answers

Onboarding became another major use case.

In one example, a newer CS Ops team member used Arovy to figure out how a field in Gainsight was generated by following the breadcrumbs back to where it originated. That kind of access to context made it easier for newer team members to understand the system without relying on someone else to explain every detail.

The same approach helped during larger data initiatives, where view access made it possible for people to see how things were interconnected across the org.

It also proved useful for Florence’s consulting partner. With access to Arovy, many of the questions that would normally require manual back-and-forth could be answered directly in the platform. That included whether a field was used in reports, what it was supposed to mean, whether it appeared in automations, who had access to it, how many page layouts it was on, and how often it was populated.

Rather than routing every question through one person, Florence could point people directly to Arovy and let them find those answers for themselves.

A Data Dictionary That Helped Identify What Was No Longer Needed

The data dictionary became another important part of the team’s workflow.

It helped clarify what fields were supposed to mean and how they were being used, but it also surfaced something less expected: fields the team no longer needed. In some cases, fields looked like they should have been useful, but once definitions were reviewed and compared to other fields, it became clear the information was already being handled elsewhere.

That helped Florence streamline parts of the org beyond what the team originally expected.

The AI-powered definitions and help text also sped things up. Instead of starting from scratch, the team could generate a first version quickly and then refine it where needed. Most of the time, the result was already close, with edits focused more on shortening the text than correcting it.

Supporting AI and Analytics Work Ahead

For Florence, this work also connected to larger AI and analytics plans.

With major initiatives ahead, the team wanted to move quickly, but also recognized that AI is only as good as the data behind it. If the data is not clean, the output becomes less reliable. In that context, the data dictionary and field documentation work became even more valuable.

The newer AI-powered capabilities in Arovy supported that effort not only by speeding up documentation, but also by helping the team identify what might still be useful, what could be purged, and what needed better understanding.

Additional Value Beyond Documentation

The value extended beyond documentation alone.

One benefit was being able to quickly understand how a field was used in automation across the org instead of manually searching through flows, validation rules, and integrations.

Another was version history. That made it possible to see who made a change, when they made it, and what the previous state was. For Florence, that was especially useful for formula fields and for understanding what had changed over time.

The team also upgraded to the Protect plan to monitor API usage. With a security-minded approach to prevention, that added visibility mattered even more as AI usage expands and more attention is placed on how data is being accessed and used.

The Impact

For Florence Healthcare, Arovy became a practical way to understand what was in the org, why it was there, and how it connected to the rest of the system.

That translated into clearer visibility across fields and automations, a more practical way to identify tech debt, faster onboarding for new team members, and easier self-serve answers for both internal users and external partners.

As Florence put it, the platform saves hours across the team.

Arovy helps teams better understand how Salesforce fields, automations, and dependencies are connected so they can reduce tech debt, support onboarding, and answer questions faster.