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We've somehow accepted walking into a procedure with no idea what it will cost us. In every other buying experience, you know the price before you commit. For bigger purchases, you might even take the time to compare options. But in healthcare, you show up with your insurance card and a vague idea of "in-network," without any clue what you'll owe until weeks later when the bill finally arrives.
The stakes are real: medical debt is the leading cause of personal bankruptcy in the US, and 70% of employees say they'd make a different care decision with accurate cost information. That gap between getting care and knowing the actual cost is the exact problem we built the cost engine to close.
Asking "how much will this cost me?" is never a simple question because the price isn't a single number. It's a dynamic outcome driven by a moving target:
Our recent Nayya survey confirms the scale of this problem: 52% of consumers do not receive an accurate out-of-pocket cost estimate. They seek answers but are given wrong numbers, partial information, or are simply told no one can give them a number.
Even a few years ago, this was not a solvable problem: negotiated rates were treated as trade secrets. Neither providers nor payers were legally required to disclose them. That changed with the federal Transparency in Coverage (TiC) Rule and the No Surprises Act, mandating that insurers publish Machine-Readable Files (MRFs) containing negotiated rates for every procedure on a provider-to-provider basis.
This was a massive win for transparency, but it created a new wall: informational chaos. The data is now "out there," but it’s buried in petabytes of raw, inconsistent JSON and XML files hidden deep in provider and carrier websites. Transparency made the data public; it didn't make it usable. To find a real answer, you first have to translate the noise.
Most tools treat healthcare as a standalone event. But real care is path-dependent: a screening leads to imaging, or a follow-up crosses a plan-year boundary. Even where you go, an ambulatory center versus an outpatient hospital, for example, can dramatically shift the price tag for the exact same procedure.
Having access to normalized data is only the first step. The real challenge, and where the industry historically fails, is in the orchestration. Knowing a negotiated rate is useless if it can't be mapped to a specific member's plan, their real-time medical utilization, and the unpredictable nature of a clinical journey.
Nayya's cost engine sits atop these massive transparency datasets and transforms them into something actionable: a grounded estimated member spend that reflects the messiness of the real world, accounts for your real-time progress in your plan year, and surfaces exactly how costs shift as care unfolds.
The engine addresses each of the challenges above by integrating three layers:
The cost engine is built on three layers that Nayya has spent 6+ years assembling: data foundations across 60 billion cost data points, benefits structure across 5M+ plans, and claims intelligence processing 15M+ claims annually. Together, they do something no general AI tool can: take a negotiated rate buried in a petabyte of raw data and turn it into an answer that knows your plan, your deductible, and your zip code.
Because the real cost of not knowing isn't just a surprise bill. It's the procedure delayed, the care deferred, the decision made in the dark. The answer has always existed somewhere in the system. We built the infrastructure to put it in your hands when it matters most.
