Hippocratic AI

Hippocratic AI develops a safety-focused large language model for patient-facing non-diagnostic clinical tasks like pre-op calls, discharge follow-ups, and chronic care check-ins. $141M Series B at $1.64B Jan 2025. $194M total. Named America's Greatest Startup Workplaces 2025 by Newsweek. App store model for healthcare AI agents.

Hippocratic AI raised $141M Series B at $1.64B in under two years with a unique safety-first positioning; it represents the bet that LLMs will handle a meaningful share of patient-facing clinical communication.

hippocraticai.com Healthcare Foundation Models

United Statesprivate

At a glance

Total raised

$387M

last round series c

Headcount

201-500

approximate · public range

Headquarters

Palo Alto

United States

Founded

2023

3 years active

Status

Private

Healthcare Foundation Models

Latest valuation

$3.5B

Series C · 2025

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Funding history

Total raised

$387M

Rounds tracked

Latest stage

Series C

Latest valuation

$3.5B

Date	Stage	Amount	Valuation	Lead investors
Nov 3, 2025	Series C	$126M	$3.5B	Avenir Growth
Jan 9, 2025	Series B	$141M	$1.6B	Kleiner Perkins
Sep 19, 2024	Series A	$17M	$500M	NVentures
Mar 18, 2024	Series A	$53M	$500M	Premji Invest, General Catalyst
May 16, 2023	Seed	$50M	—	General Catalyst, Andreessen Horowitz

Founder backgrounds

Munjal Shah

Co-Founder & CEO

A serial entrepreneur with a 25-year arc spanning e-commerce, computer vision, and healthcare. Shah graduated from UC San Diego with a computer science degree, where he researched neural networks before applying them to HIV drug design at Agouron Pharmaceuticals. He founded Andale (e-commerce tools for eBay sellers, acquired by Alibaba), then Like.com (visual search via computer vision, acquired by Google in 2010 for a reported $100M+). His next venture, Health IQ, a Medicare brokerage, collapsed amid lawsuits alleging millions in unpaid debts and vendor fraud—yet a16z, which had backed Health IQ, reinvested in Hippocratic AI. That pattern—backing Shah despite controversy—is a distinguishing mark of his career. He founded Hippocratic AI in 2023 after concluding generative AI had finally reached a safety threshold for non-diagnostic clinical tasks.

Subhabrata Mukherjee

Co-Founder & Chief Science Officer

Earned a PhD in computer science summa cum laude from the Max Planck Institute for Informatics, where his dissertation earned the 2018 SIGKDD Doctoral Dissertation Runner-Up Award. Worked as a Principal Researcher at Microsoft Research Redmond and held prior AI research roles at Amazon (where he helped build the Amazon Product Knowledge Graph), IBM Research, and Google. Before co-founding Hippocratic AI, he led large-scale AI initiatives across these labs, working on language models, knowledge graphs, and information extraction. His academic and industry research focused on factuality and grounding in language models—themes that directly carry into his current work building safety-aligned clinical LLMs for patient-facing tasks.

Vishal Parikh

Co-Founder & Chief Product Officer

Prior to Hippocratic AI, Vishal Parikh was Co-Founder and Chief Product Officer at Health IQ, a health-focused life insurance startup that used data and AI to personalize rates for health-conscious individuals. He also co-founded Health Equity Labs. His academic background is in electrical engineering, with studies at Stanford University and The University of Texas at Austin. At Hippocratic AI, he leads product and engineering across the company's technology stack including Polaris, infrastructure, interfaces, and tools — applying experience from scaling data-driven health ventures to a safety-first clinical LLM for non-diagnostic patient communication.

AI bull / bear

Bull case

Hippocratic AI attacks a genuine pain point: health systems spend billions on routine patient communication that clinical staff find repetitive and burnout-inducing. Its safety-first LLM, purpose-built for non-diagnostic workflows like pre-op instructions and chronic care check-ins, gives it a defensible wedge in a market where generic models such as GPT-4 or Claude carry unacceptable liability risk. The $126M Series C at a $3.5B valuation—roughly doubling in under a year from the Series B—signals that Avenir Growth saw operational traction worth backing aggressively. The app-store model is the strategic prize: if Hippocratic becomes the platform where specialized healthcare AI agents are built and clinically validated, it accrues network effects that even Epic or Cerner would struggle to replicate. With $387M in total funding, the company has the balance sheet to endure long enterprise sales cycles, invest in clinical evidence, and outlast undercapitalized point-solution competitors. Healthcare AI desperately needs a safety brand, and Hippocratic is staking that claim first.

Bear case

A $3.5B valuation demands an extraordinary outcome: Hippocratic must win a material share of a market that incumbents already serve and that regulators could constrict. Epic's MyChart reaches over 100 million patients and is incorporating AI features directly; most health systems will prefer a single-vendor solution over a third-party platform that introduces integration risk and a new attack surface. The "non-diagnostic" boundary is fragile—if regulators determine that a post-discharge follow-up asking about symptoms constitutes triage, Hippocratic's entire safe harbor narrows. One adverse patient event, even if statistically explainable, could freeze enterprise adoption for multiple quarters. The app-store model, however elegant, assumes health systems will allow third-party AI agents to touch protected patient data, a proposition most CISOs and compliance officers will resist fiercely. At $387M raised across several rounds, late-stage investors need a $10B-plus exit, which requires winning against both well-funded AI labs and entrenched healthcare IT incumbents simultaneously.

Generated by deepseek-v4-pro · May 2, 2026Refreshes quarterly · see disclosures

In the news

Hippocratic AI (official)4/30/2026
Hippocratic AI Launches Polaris 5.0: The First Evidence- based AI for Healthcare Proven to Outperform Major Frontier Models on Critical Medical Tasks and Safety
Hippocratic AI launches Polaris 5.0, a healthcare-specialized voice AI agent benchmarked to outperform major frontier models on clinical accuracy, HIPAA compliance, and patient safety.

Watch

Leadership

Munjal Shah

Co-Founder & CEO

in 𝕏

Vishal Parikh

Co-Founder & Chief Product Officer

—

Subhabrata Mukherjee

Co-Founder & Chief Science Officer

—

Anan Kashyap

Chief Financial Officer

—

Peter Casasanto

Chief Commercial Officer, Pharma

—

Rahul Agarwal

Chief Customer Officer

—

Sejal Rajagopalan

Chief People Officer

—

Dr. Shubhra Jain

Chief Business Officer

—

Products

Polaris
Polaris is a safety-focused large language model constellation purpose-built for real-time patient-AI healthcare conversations. It comprises a suite of specialized LLMs that have been trained using organic and simulated healthcare conversations reviewed by U.S.-licensed clinicians. Polaris handles non-diagnostic patient-facing tasks such as pre-operative calls, discharge follow-ups, chronic care check-ins, and medication adherence monitoring. It outperforms frontier models on safety, empathy, and clinical compliance benchmarks, and is validated against over 180 million real patient interactions.
AI Front Door
AI Front Door is a single AI-powered entry point that triages and routes patients to the appropriate care setting or service within a healthcare organization. It serves as the first point of contact for patients seeking care, intelligently handling scheduling, symptom assessment, and care navigation without requiring human intervention. The product is designed to reduce administrative burden on healthcare staff while ensuring patients are directed to the right resources quickly and safely.
Nurse Co-Pilot
Nurse Co-Pilot is a patient-facing voice AI agent designed to assist nurses by handling routine clinical communications autonomously. It conducts outbound and inbound patient calls for tasks like post-discharge follow-ups, pre-procedure instructions, chronic disease management check-ins, and medication reconciliation. The agent uses motivational interviewing and empathetic communication techniques to engage patients effectively, and can escalate complex cases to human nurses when needed, allowing clinicians to focus on higher-acuity care.

Most recent patents

HealthGPT, Inc. DBA Hippocratic AI·2025-07-22
Low-Latency Conversational Artificial Intelligence (Ai) Architecture With A Parallelized In-Depth Analysis Feedback Loop
US12367971
Abstract
Low-Latency Conversational Artificial Intelligence (Ai) Architecture With A Parallelized In-Depth Analysis Feedback Loop
US12367971
Abstract In some aspects, an artificial intelligence that is configured to complete a checklist during a conversation initiates the conversation with a human. The artificial intelligence receives a first human response from the human, extracts data from the first human response, and stores the data in a conversation summary. The artificial intelligence provides a first artificial intelligence response to the human. The artificial intelligence receives a second human response from the human, accesses the conversation summary, and provides a second artificial intelligence response to the human based at least in part on a portion of the data in the conversation summary. The artificial intelligence, based at least in part on determining that the checklist has been completed, ends the conversation with the human.
View on Google Patents
HealthGPT, Inc. DBA Hippocratic AI·2024-11-12
Low-Latency Conversational Artificial Intelligence (Ai) Architecture With A Parallelized In-Depth Analysis Feedback Loop
US12142371
Abstract
Low-Latency Conversational Artificial Intelligence (Ai) Architecture With A Parallelized In-Depth Analysis Feedback Loop
US12142371
In some aspects, a multi-turn conversational system includes: an artificial intelligence to provide a conversation interface configured to execute multiple turns of human-like conversation with a user and a control logic, in communication with the conversation interface, and configured to generate one or more control signals based on evaluating multiple turns of upstream human-like conversation between the conversation interface and the user. The control signals contribute in part to construction of multiple turns of downstream human-like conversation between the conversation interface and the user.
View on Google Patents

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