A small San Francisco team pointed deep learning at the least glamorous problem in senior care: the paperwork behind every CMS survey. It turns out that is worth a lot.
The wordmark of a compliance company. Clearpol builds its own HIPAA, SOC 2 and SOC 3 stack before asking a single nursing home to trust it with a resident's chart - the least flashy competitive moat in healthtech, and one of the hardest to fake.
Here is a fact about American nursing homes that nobody puts on a brochure: the single most stressful document in the building is a government inspection form called CMS 2567. Clearpol built an AI to read it, answer it, and - the company says - get the answer accepted on the first try, every time.
The way regulatory compliance is supposed to work in a skilled nursing facility is that surveyors from the Centers for Medicare & Medicaid Services show up, inspect, and write down deficiencies on Form 2567. The facility then writes a "Plan of Correction" - a POC - explaining exactly how it will fix each problem. If the POC is any good, life goes on. If it is not, the facility gets more scrutiny, more visits, and, eventually, more risk to its Medicare and Medicaid funding.
This is a miserable genre of writing. It is legalistic, high-stakes, and produced by people - directors of nursing, administrators - who would much rather be caring for residents. It is, in other words, exactly the kind of task that large language models are unreasonably good at: bounded, document-heavy, and enormously valuable to get right.
Clearpol's pitch is that its POC Writer takes the citation on Form 2567 and generates a Plan of Correction aligned with what surveyors actually expect. The company reports a 100% acceptance rate on the POCs it submits. You should read a self-reported 100% of anything with a raised eyebrow - it is a marketing number, not an audited one - but the underlying claim is the interesting part: get the boring document right the first time, and you have removed the single scariest recurring event from a facility's calendar.
That is the whole thesis, really. Clearpol is not trying to replace the nurse or diagnose the patient. It is trying to absorb the regulatory load - the reading, the drafting, the "wait, what does the rule in this state actually say?" - so that the humans can spend their scarce hours on residents instead of binders. The company describes its product as a compliance "co-pilot," which is the correct amount of ambition. Co-pilots do not fly the plane. They just make sure you do not miss anything.
What makes this more than a one-trick document generator is the rest of the stack. Clearpol Clinical AI reads a facility's progress notes, labs and orders around the clock and flags a resident whose numbers are drifting before the drift becomes a citation. Insights Pro carries a nationwide regulatory database across all 50 states, plus a chatbot trained on those rules that can draft a policy on request. Underneath all of it sits the least sexy and most important thing a healthcare startup can own: HIPAA, SOC 2 Type II and SOC 3 compliance.
Clearpol sells to skilled nursing facilities, post-acute operators and, increasingly, entire state associations. The modules stack into something the company wants to be the operating system for compliance.
Continuous automated review across progress notes, labs and orders, with real-time alerts on vitals, medications and risk trends surfaced on a prioritization dashboard for clinical teams.
Automated generation of CMS Form 2567 Plans of Correction - regulator-aligned documentation the company says is accepted on the first submission.
A nationwide regulatory database, weekly update alerts, and an AI chatbot trained on federal and state rules that drafts compliance policies on demand.
Facility Assessment, Infection Line List, Clinical Dashboard, Resident Center, audit tools and survey-intelligence features for day-to-day readiness.
*Company-reported, not independently audited.
Clearpol was co-founded by Arash Hosseini Jafari (CEO) and Faddy Sunna. Before compliance software, Jafari's resume ran through NASA's Jet Propulsion Laboratory and Langley Research Center, plus UC Irvine Health - a deep-learning-meets-clinical background that reads oddly well for a company built to parse regulations.
The through-line is systems where a small miss compounds into a large failure. Rockets are one. A survey-day deficiency that snowballs into lost Medicare funding is another. Both reward software that never gets tired of reading.
Profile compiled from public sources including clearpol.com, McKnight's Long-Term Care News, McKnight's Senior Living, Crunchbase and PitchBook. Traction figures (900+ facilities, 3,000+ POCs, 100% acceptance) are self-reported by Clearpol and not independently audited. Funding figures vary across sources - a $400K raise is recorded for May 2022, while some reports cite roughly $3.3M in total seed capital; treat these as approximate. Details current as of July 2026.
Clearpol is a San Francisco healthtech company that builds AI tools for post-acute and long-term care providers. Its platform reads a facility's clinical data and regulatory environment, then automates the paperwork nobody likes: drafting CMS Form 2567 Plans of Correction, flagging resident risk before it escalates, and answering questions about federal and state nursing-home regulations. Positioned as a compliance 'co-pilot' for skilled nursing facilities, Clearpol says it is trusted by 900+ long-term care facilities and has written thousands of Plans of Correction.
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