Pomegra
Baba, a New York AI care-coordination startup, closed a $40M Series A led by FirstMark Capital, with CVS Health Ventures joining, to scale Medicare and Medicaid support.
Key Takeaways
- Baba announced a $40M Series A on October 20, 2026, led by FirstMark Capital; valuation was not disclosed.
- CVS Health Ventures and Valtruis joined; General Catalyst, Genius Ventures and Ground Up Ventures returned.
- The company says it has served 10,000+ patients and has a waitlist above 100,000 families.
Lead
Baba, a New York company that pairs human care coordinators with AI agents to manage Medicare and Medicaid patients' care, announced a $40M Series A on October 20, 2026. FirstMark Capital led the round. CVS Health Ventures and Valtruis participated, and existing backers General Catalyst, Genius Ventures and Ground Up Ventures also took part. Baba did not disclose a valuation.
The raise follows a seed round of more than $6.5M and marks the company's exit from stealth. Founder and CEO Connor Sweeney, an MIT graduate in biological engineering and computer science, runs the company. Baba says it has supported more than 10,000 patients and families across the United States.
What Does Baba Actually Do?
Baba assigns Medicare and Medicaid members and their families a dedicated care team made up of care coordinators and licensed therapists, backed by AI agents that handle the administrative work. That work includes syncing referrals between specialists, managing discharge transitions, arranging in-home aides, disputing medical bills and locating community resources.
The company is paid through partnerships with health plans and value-based care arrangements, not by patients. Its partners include national commercial and Medicare Advantage plans, as well as the American Stroke Foundation and the American Breast Cancer Foundation. The pitch to insurers is that cheaper coordination makes it viable to serve patients who have been too expensive to support with traditional case management.
Sweeney framed the thesis this way: "By dramatically lowering the cost of operating and coordinating care, we have an opportunity to make care delivery economically viable" for populations that have historically been too costly to serve. That is a claim about unit economics. Baba has not published margins, cost per member or outcome data that would let outsiders test it.
Why Did FirstMark and CVS Back the Round?
FirstMark treats Baba as a consumer-health bet aimed at a specific demographic. Rick Heitzmann of FirstMark described the company as "our latest bet on the consumerization of healthcare, this time for millions of Americans who are rural, older and have more complex needs."
CVS Health Ventures, the venture arm of a company that owns Aetna, has a more direct commercial interest. Carter Prince of the fund said that by training on specific long-horizon activities and keeping humans deeply in the loop, Baba could help more patients get the care and support they need. A strategic investor in the round also hints at a possible future customer or partner, though no commercial agreement with CVS was announced.
How Large Is the Waitlist Signal?
Baba reports a waitlist of more than 100,000 families against roughly 10,000 patients served, a ratio of about ten to one. The figure shows demand but also shows a capacity constraint: the company can onboard patients only as fast as it hires and trains clinical staff. Waitlists are also a soft metric, since they measure interest and not paid, enrolled members.
That explains the stated use of proceeds. Baba plans to expand its clinical care teams, deepen AI research and add health plan partnerships. Hiring clinicians scales more slowly than software, which puts the burden on the AI layer to raise each coordinator's caseload without degrading quality.
What Is BabaBench and the CMS ACCESS Model Role?
BabaBench is Baba's own benchmark for evaluating how well AI agents carry out long-horizon healthcare workflows, such as multi-week insurance calls, billing appeals and prior authorization disputes. It enforces privacy safeguards around protected health information. The company has published the benchmark on arXiv, and it was accepted for presentation at a NeurIPS workshop.
Separately, the Centers for Medicare & Medicaid Services selected Baba as an operating participant in its ACCESS Model, a 10-year chronic care initiative, across all four clinical tracks: Early Cardio-Kidney-Metabolic, Advanced Cardio-Kidney-Metabolic, Musculoskeletal Health and Behavioral Health. Participation gives Baba a government-sanctioned route to patients with chronic conditions, which is where coordination spending is highest.
Who Is Joining the Team?
Baba added Alicia Tennenbaum, a licensed clinical social worker formerly at Mount Sinai, as Executive Director of Clinical Operations. It also hired Alan Li, previously at Ramp, as a product designer. The clinical hire fits the capacity problem: the near-term constraint is clinical operations, not model quality.
What Comes Next for Baba?
The company now has to turn a long waitlist into enrolled, paying members without letting service quality slip. Health plans tend to run slow procurement cycles, and AI-assisted care touching billing disputes and prior authorizations will face scrutiny over privacy and accuracy. The ACCESS Model participation and a strategic investor from the insurer world both help on credibility.
Competitors range from traditional case management vendors to other AI-enabled navigation startups, and incumbents inside large insurers. A $40M round, about six times the seed, gives Baba runway to hire ahead of demand, but it is modest next to the capital raised by larger care-delivery companies. The test is whether published outcomes data follows the benchmark.
Outlook
Baba enters its Series A phase with a lead investor from consumer-health venture, a strategic backer tied to a major insurer, and a CMS program slot across four clinical tracks. What remains unproven is the cost structure behind the pitch: how many patients a coordinator can handle with AI support, and whether health plans will pay for it at scale. Evidence on cost savings and member outcomes will decide whether the waitlist converts.
