How Medely Drafted 75% of Its Q3 Board Deck Two Days After Quarter End

About Medely

Medely is a healthcare workforce orchestration platform. The company was founded after Angie Nasr, a Director of Nursing, brought the daily challenges of manually scheduling and credentialing clinicians to Waleed and Khaled Nasr, who paired her frontline experience with their technology background to build a modern solution.

What started as a marketplace matching facilities with per-diem talent has grown into a full workforce orchestration platform:

• 300K+ clinicians connected to flexible work opportunities
• 2M+ shifts filled across hospitals, health systems, and outpatient care facilities
• 94% fill rate for the facilities Medely serves
• Joint Commission Gold Seal of Approval earned for qualified-professional standards

Q3, two days after quarter end

On October 1, Medely's accounting books were still open. Tom didn't need them even soft closed to start analyzing price and volume. Medely's platform data, which records every shift down to price and hours, lands in Snowflake twice a day, so using Delphina he could break out price and volume by segment in minutes and see what drove the quarter.

The accounting close ran on its normal schedule. The analysis simply didn't wait for it. By October 2 he had about 75% of the deck drafted, built around fewer numbers and a more narrative storyline. Before Delphina it would have taken well over a month.

He had used the same process in August for his first board meeting. "Our founders described it as 'the best board meeting we've ever had,'" he says.

A finance leader who knows the queries

Tom has been at Medely for almost five years. He built the working capital side of finance, served as controller, and today he serves as Head of Finance with FP&A and BI reporting to him. He describes himself as an ex-soldier turned ops leader turned finance leader, and he taught himself SQL and Tableau to get his teams the data they needed.

He did not start as a believer. His first question to Delphina was "How do I order a pizza?" It gave a bad answer, and the exchange became a running joke inside Medely.

Teaching Delphina Medely's definitions

Medely reports revenue two ways, an operating view tied to shifts and an all-in view, and finance needed Delphina to keep them straight. Early on, Tom wrote out the company's core metrics in plain English, along with the Postgres query he used for revenue. He loaded that file into Delphina's knowledge agent, and Delphina worked out the Snowflake equivalents. He also uploaded spreadsheets from the seven past board decks.

Then he checked it against the ledger. "It's all about the code. In mine, I know exactly what numbers it's pulling," he says. Revenue pulled through Delphina came within about $20,000 of NetSuite, well within his materiality threshold. That was what won him over. "It tied out to my actual financial data," he says.

Close isn't good enough in finance. When Tom tried connecting a general-purpose AI assistant straight to NetSuite, its answers were about 90% right, so he disconnected it. "Even 95% right isn't good enough for your financials. Delphina is different."

A week of analysis in a couple of prompts

In July, Tom needed to compare the first half of 2026 with the first half of 2025. FP&A typically spends a week on that comparison. In Delphina, it took him a couple of prompts. "It can take the data and say, 'This is what really happened, and this is what changed,'" he says. "That, to me, is just mind-blowing."

He still ties the output to the books, and he expects Delphina to tell him when something is off. "If it would have seen, 'Hey, this isn't tying out, Tom,' I know it would have told me."

A retention model in a weekend

Tom spent his first month or two pressure testing Delphina. After that, he built next year's cohort retention baseline revenue model in a single weekend. FP&A normally spends about a month on that model offline in Excel. The team reviewed his version and judged it close to what they would have produced.

The morning dashboards

Tom starts each day with three or four Delphina data apps he built himself. They cover cash collections, the month's sales forecast against budget, and the Stripe account Medely tops up a couple of times a week to pay its clinicians.

"I trust Delphina's forecast more than I trust our own queries and old calculations," he says.

What's next

For Tom, the bigger change is what finance gets to spend its time on. "You spend all this time reporting the past, and then you can't influence the future," he says. "Delphina shortens and simplifies, and now we can really impact what's next." When a colleague said, "Imagine if we had this two years ago," Tom's answer was, "Imagine what two years from now it's gonna look like."

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