Handl Health is building an AI platform that aggregates and analyzes publicly available healthcare pricing data, helping brokers and benefits consultants design, evaluate, and personalize health plans for self-insured employers.
In a recent episode of BUILDERS, we sat down with Ria Shah, Co-founder & Chief Product Officer of Handl Health, to learn how the company turned newly mandated price transparency data into a plan design platform that brokers and benefits consultants rely on.
**Topics Discussed:**
- How price transparency legislation created a now-or-never moment to build on terabytes of newly published contracted rates
- Why neither co-founder was technical, and how Ria learned Python and PySpark to ingest 300 billion row machine readable files
- How an NIH grant and a free consumer cost estimator revealed the wrong initial customer
- Why Handl Health moved past direct-to-employer sales to the broker and benefits consultant channel
- Where brokers see value first: prospecting new business with carrier comparisons and personalizing renewals with claim-level cost projections
- The three-year education arc from explaining what an MRF is to a market where everyone needs a price transparency partner
- How Handl Health escaped the checkbox compliance perception by layering actuarial modeling and steerage on top of public data
- Why white glove service remains core even as the company productizes a services-heavy workflow
**GTM & Technology Adoption Lessons:**
- Follow the burden until you find the buyer who owns it: Handl Health started consumer-facing with a free cost estimator, then realized that reaching the masses meant going through self-insured employers, who hold majority market share in how most Americans access healthcare. But employers are overburdened and understaffed, so the company went to the brokers and benefits consultants those employers already trust.
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- Sell to the channel that is already on the hook: Brokers must give sound recommendations to employer clients about which network to rent and which point solutions to buy, while working with disparate data and no ROI visibility on vendors. When they started using the platform, Ria said their reaction was "wait, we can do this in minutes." Adoption is fastest where accountability and pain already sit together.
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- Market education runs in stages, then flips: Ria described years one to three as skepticism that the regulations would even hold and that the data was useful. The company went from explaining what an MRF was, to convincing the market that MRFs contain useful data, to a market where everyone needs a price transparency partner and the only question is which one. Early adopter champions carried the company through the skeptical years.
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- Regulation creates data, not a category: Early on, products like Handl Health's were perceived as checkbox compliance costs. The escape was the layer above the public data: actuarial modeling, cost projections, and steerage assumptions that help employers bring down costs and help members shop for higher value, lower cost care.
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- Align the commercial model with channel growth: Ria said the commercial model came down to aligning incentives. As long as the platform helps brokers grow their book of business and differentiate in the market, they keep coming back and find new ways to partner.
- Productize the workflow, keep the concierge: Handl Health is productizing a highly services business. AI tooling automates much of the analytics, but when brokers run reports or stratify networks, the team wraps their arms around those partners to interpret results together. Ria does not expect that to go away, because the white glove layer is what makes the channel stick.
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