Marisa.Care
Hub of conversational AI agents for hospitals, operating across three orchestrated layers: triggers, where agents identify clinical gaps and risks from health assessments, prescriptions, and diagnostic findings; navigation, where agents qualify patients and guide care journeys; and revenue, where pending care is converted into booked services. Patients calling or messaging reach conversational AI with no menu and no IVR, and the agent accesses the real schedule to resolve the request end to end. After a visit an agent reads the medical record, identifies prescribed exams and consultations that were never booked, and proactively offers scheduling over WhatsApp, recapturing care that would otherwise be lost to follow up. Other agents confirm appointments with preparation guidance to reduce no shows. WhatsApp as the primary channel reflects the market: the company serves reference hospitals in Brazil and Mexico and is scaling across Latin America, which makes it one of the few vendors in this index built for a non US care and communication context. Pre seed stage; founded by Sergio Ricardo Santos.
Capability Axes
Conversational agents are the product across all three layers. The company's stated design point, no menu and no IVR with the agent accessing the real schedule and resolving requests end to end, is only achievable with the model doing the work rather than routing to it.
The agents operate autonomously on live patient conversations, booking against the real schedule and initiating outreach based on record contents, with no escalation path, human review step, or handoff criteria disclosed in retrieved materials. Proactive outbound contact triggered by a patient's diagnostic findings is a consequential action, and the absence of stated guardrails around what the agent may raise unprompted is the gap a buyer should close first.
Deployment at reference hospitals in Brazil and Mexico is stated, which satisfies commercial availability, but no customer names, volumes, conversion rates, or no show reduction figures were retrieved. The company is pre seed. The core mechanic, reading the record to find prescribed exams that were never booked and offering them proactively, is a sound and specific idea, but its effect size is unquantified.
No public pricing. Enterprise sales to hospitals. The value framing is revenue recapture per converted pending item and reduced no shows, which suggests outcome or conversion linked economics, though the model was not disclosed. If pricing is conversion linked, buyers should examine how aggressively agents pursue booking, since the incentive runs toward volume.
Scope is clear, hospital patient access and care gap recapture, and the geographic focus is a genuine differentiator rather than a limitation: the company serves reference hospitals in Brazil and Mexico and builds for WhatsApp as a primary clinical communication channel, which reflects how patients in those markets actually reach providers. Held back from A because the model's applicability outside Latin American communication norms and payer structures is untested.
Pricing
Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.
| Entry Price | Pricing Basis | BAA Tier | Implementation | Source |
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Contact the vendor
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Enterprise hospital agreements; likely conversion or outcome linked | — | — | Third Party Estimated |
No public pricing. Enterprise sales to hospitals. The company frames value as revenue recaptured per converted pending exam or consultation and reduced no shows, which suggests outcome or conversion linked economics, though the model was not disclosed. If compensation scales with bookings, the incentive runs toward volume, and buyers should establish what limits exist on unsolicited outreach.