The people who make healthcare's hardest calls run on Kai.
From a public healthcare company to the bankers, operators, and dealmakers who serve the industry, Kai turns the data that's hardest to get into the decision, where one wrong number costs millions.
Find the revenue. Cut the cost. Underwrite the call. Catch the risk.
Eight real sessions, anonymized, each with the call it teed up and the number Kai put on the table. Whatever you do in healthcare, the decisions are the same. Verbatim, not rounded.
Find your whole pipeline.
$52.5M
sized across 20 named targets, in one session
A medication-automation vendor asked Kai to find every facility that should be buying. It built the list: 20 targets, the units each one needs, the decision-maker to call, the clause to displace the incumbent, the open RFPs. A year of quota in one session.
Applies to any company selling into healthcare: devices, pharma, services, software.
From CMS provider data, facility filings, and live federal and state RFP boards.
Find the money hiding in your numbers.
$676K/yr
in one building. Most operators run dozens
A multi-facility operator handed Kai one building's messy P&Ls and census. It found $676K a year of overspend to cut, a two-year path to repay investors, and the Medicare penalty hiding in the math. That is a single building.
Applies to any operator, plan, or provider with a P&L and a payroll.
From the operator's own files, benchmarked against state cost reports and CMS data.
Win the Medicare Advantage play.
$15M
five-year swing, modeled on real county data
Kai modeled an I-SNP against fee-for-service on live county data: capturing the Medicare spend flowing to dual-eligible members was worth as much as $15 million over five years. The math that decides a payer strategy.
Applies to plans and operators deciding where the Medicare dollars should flow.
From CMS county Medicare Advantage data, reimbursement rates, and plan economics.
Underwrite any deal in minutes.
17.2x
ten-year return, modeled and stress-tested
A senior-housing investor asked Kai to model a recap on a $6M deal. It built the bridge-to-HUD: $7.9M cashed out at the Year-3 refinance, a 17.2x ten-year return, the downside laid out. The work an analyst bills a week for.
Applies to investors, lenders, and corporate development, in any sector.
From the deal's economics, HUD refinance terms, and comparable transactions.
Walk into any room the expert.
$173.6B
market mapped before the call
A commercial banker covering health tech used Kai to prep two prospects: the home-care market sized at $173.6B, each company's likely next raise, the reason to leave the incumbent. He walked in the most informed person there.
Applies to anyone walking into a meeting they need to win: sales, banking, BD.
From market research, company filings, funding records, and live signals.
Catch what a human would miss.
580 pages → 1 brief
triaged for safety, in one pass
A multi-facility operator gave Kai 580 pages of overnight nursing reports. It caught a blood sugar of 508 left undocumented, 16 refused insulin doses, and a dangerous drug interaction. Patient safety, at a scale no one can read by hand.
Applies to any team drowning in reports: clinical, compliance, claims, risk.
From the facility's own PointClickCare reports.
Stop the revenue walking out the door.
$3M/yr
lost to missed calls, found in the data
A behavioral-health operator asked if intake was leaking money. Kai tied the missed calls to its own conversion rate and payer mix: as much as $3 million a year, with PPO payers, gone because nobody picked up. The reply: I want to build this.
Applies to any provider that lives on referrals, intake, and conversion.
From the center's intake data and its real reimbursement and conversion rates.
Size any market before you commit.
$108.7B
market sized, whitespace mapped
Kai sized the home medical-equipment market at $108.7B by 2033, mapped where it is fragmented, and named who is consolidating. The landscape a research firm charges five figures and six weeks for, in one answer.
Applies to pharma, device, and health-tech teams sizing a move.
From company filings, market data, and the competitive record.
Making decisions across healthcare. Names on request, with consent.
A VP in a global bank's healthcare group opened Kai for the first time and typed one cold prospecting question. Fifty minutes later, with no setup and no training, he had the work a junior banking team spends two days on: a market map, a list of the vendors, deep dives on named prospects, and a client-ready briefing memo. Then he told his team it “should be deployed against our entire healthcare division.”
He reached for Kai even though the bank already had its own AI, because the healthcare data and know-how just aren't in a general model. That's the pattern everywhere: people have their own AI, and still reach for Kai when there's a healthcare decision on the line.
“There's zero comparison to a general AI. The detail and research I got from one session would rival hundreds of hours with a healthcare-specialty investment-bank team.”
“It can do a lease abstract and put a single-tenant deal proposal together in minutes, versus what could be an entire workday of underwriting. We'll be using it for all our proposals from now on.”
“Way more detail on healthcare than anything from ChatGPT, Copilot, or Gemini. You can't get competitive landscapes, market signals, and industry insights like this anywhere else.”
“This market analysis would cost me between eleven and twenty thousand dollars in normal cases.”
“Something that would take me five hours to do, it took thirty seconds.”
“It's kryptonite for every broker. The offering memos we look at are doctored, the only question is the slant. This is like seeing behind the kimono.”
“ChatGPT gives three-line answers with very little detail. This had explicit detail, real depth of knowledge.”
“With Kai, it almost corrects itself, I'm not putting outdated information together anymore.”
“I can't find benchmarks for these things anywhere else.”
“This should be deployed against our entire healthcare division.”
Quotes are real and anonymized pending consent. Attributions shown with permission.
40%
Better than Claude, ChatGPT, Gemini & Copilot on healthcare intelligence, depth, and reasoning, the gap that decides which answer they act on.
< 90 days
A public healthcare company found Kai, tested it against a general-AI tool, passed full security review, and signed, dropping what they used before.
Daily
A VP in a global bank's healthcare group uses Kai himself, and is pushing it across the division, even though the bank has its own AI.
100% inbound
Thousands of healthcare professionals across hundreds of companies have used Kai, without us ever reaching out to them.
Make your next call on Kai.
Healthcare data exists everywhere. Point Kai at your own, and it turns it into the decision, every answer cited to its source.