Does AI hold up in retrospective risk adjustment once real customers put it to work? KLAS Research went and asked. RAAPID was featured in the May 2026 KLAS Emerging Company Spotlight, RAAPID AI Retrospective Risk Adjustment 2026: Enhancing Risk Adjustment Coding Defensibility Through Neuro-Symbolic AI Solutions, an independent read of what customers experience day-to-day. Here is what came back.
Customer grades (Emerging Data, n=5)
- Would buy again: A+
- Likelihood to recommend: A
- Partnership: A
Every interviewed customer said they would buy RAAPID again.
For our founder, that result reflects an early choice, not a response to this year's headlines.
"Defensibility has been the foundation of RAAPID's risk adjustment platform from day one, not a feature we added when the regulatory pressure arrived. We built our entire solution around it: Neuro-Symbolic AI that links code to verbatim clinical evidence, explains every recommendation and produces documentation that holds up under RADV review. When KLAS interviewed customers, they gave an A+ would-buy-again grade, which is confirmation that we've built what risk adjustment leaders actually need." — Chetan Parikh, Founder and CEO of RAAPID
How customers use the platform (Emerging Data, n=5)
Three capabilities show up across the customer base, and together they explain the grades.
- DocumentAI, the proprietary OCR that reads PDFs and scanned records at high fidelity, even thousand-page charts.
- Neuro-Symbolic AI traceability, where every code surfaces its own clinical logic, chart evidence and justification.
- Single-pass workflow that validates new HCCs and flags unsupported codes in the same review.
Read together, they describe one way of working. The platform reads the records that other tools struggle with, shows the evidence behind every code where an auditor can see it and does both halves of the job in one pass: it adds supported codes and removes unsupported ones. The report records this as a two-way workflow built for RADV audit readiness.
Measurable outcomes
The spotlight tracked results, too. Every customer measured coding-accuracy gains, and four of five saw outcomes within six months.
In a customer's words
One executive described checking RAAPID against the tool their team already used:
"The Neuro-Symbolic AI is very accurate. We did a second-level review on some of the documents that our NLP also picked up, and we found additional opportunities. The general feedback from our coders was that RAAPID's tool handled inpatient records really well compared to our primary tool. There are thousands of pages in those records, and the tool had to review all of those, but it was able to accurately pinpoint the evidence a high percentage of the time, reducing the amount of work needed. The overall platform, including some of the project management aspects, is very well laid out." — VP, collected by KLAS Research, May 2026
Why customers choose RAAPID
When KLAS asked what drove the selection, the reasons clustered into five:
- AI accuracy you can defend in an audit
- Responsive partnership, not a black-box vendor
- Workflow efficiency at scale
- A human-centered support model
- Transparent, competitive pricing
Those five lines describe a vendor that customers trust with submission data and choose to remain with as a strategic partner.
An Emerging Company Spotlight is early-stage data, a first read rather than a final verdict. That is the right way to read it, and the reason to read the whole thing. Every customer grade and all of the KLAS commentary are in the report below.
Download the KLAS Emerging Company Spotlight.
Source: KLAS Emerging Company Spotlight, RAAPID AI Retrospective Risk Adjustment 2026 (May 2026). Emerging Data, n=5. Grades reflect the KLAS software grading scale.
