Top 9 Companies Building AI Agents in Healthcare 2026

In healthcare, AI agents have quickly become a buzzword.

Ask ten people what they mean by it, and you’ll hear ten different answers.  AI agents mean different things to different healthtech leaders. While some view them as a practical advance, others remain skeptical of the value behind the claims.

The truth sits somewhere in between.

AI has come a long way in a short time. It can handle real work now, but it’s not a sci-fi sidekick. AI agents are powerful tools when designed well, they extend human capability without pretending to replace it.

Table of Contents

• What are AI agents in healthcare?
• Which companies are actually good at building AI agents for healthcare?
• How we selected the companies in this guide
• Puppeteer AI
• Hyro
• SuperDial
• Hippocratic AI
• Kore.ai
• Parakeet Health
• EliseAI (HealthAI)
• Beeline Health
• Clearstep
• Conclusion

What are AI agents in healthcare?

AI agents in healthcare are intelligent, semi-autonomous systems built to support clinicians, care teams, and operations. They help automate routine tasks, surface relevant insights from complex data, and tailor interactions to individual patients. The result is better use of clinical time, more consistent workflows, improved diagnostic support, and patient experiences that feel responsive rather than scripted.

To give some context on the number; Industry forecasts estimate the global AI in healthcare market will grow from roughly USD 20+ billion in the mid-2020s to several hundred billion dollars over the next decade, driven by operational pressure, clinician shortages, and rising patient demand. Agent-based AI is one of the fastest-growing segments, with projections showing strong double-digit annual growth through the early 2030s.

Now that the potential is clear, the real question becomes more practical:

Which companies are actually good at building AI agents for healthcare?

That’s the focus of this article. We’ll examine the companies working in this space, break down their strengths and limitations, and explain the tradeoffs you’re making with each approach. Throughout the article, we’ll use AI agents and agentic AI interchangeably.

How we selected the companies in this guide

Every company included in this article meets the following criteria:

• A clear and sustained focus on healthcare, not “all industries”
• HIPAA compliance built into the core architecture
• Proven real-world deployments, not just polished demos
• Flexibility to adapt to different provider workflows and care models
• Human-like conversational flows that build patient trust
• Integration capabilities with EHRs and CRMs
• Transparent business models and delivery terms
• Multi-language support and accessibility by design

Whether you’re evaluating the best company for building enterprise AI agents, comparing best custom AI agent development companies, or looking for the best AI agent company to support clinical operations, this guide is designed to help you make a grounded, informed choice.

1. Puppeteer AI

Focus: Custom healthcare AI agents for patient calls, SMS, and other operational workflows.

Description: Puppeteer AI stands out as a healthcare-first AI agent builder that’s clearly designed for the messy reality of patient access: multiple channels, incomplete intakes, high call volume, and teams that can’t afford another tool that lives outside the workflow. Their product is built around ready-to-deploy care agents (intake, scheduling, triage, checkups, medical Q&A, reactivation) plus a “build your own” option when you need something more specific. They also highlight a dashboard for engagement metrics, flagged cases, and reporting, which suggests they’re thinking beyond demos and into day-to-day operations.

• Compliance: HIPAA compliance, SOC 2 Type 2.
• Years of experience: Founded 2023.
• Type of client: Mid-market clinics, specialty groups, digital health teams that want workflow-specific agents
• Range of costs: Platform fee + Usage Costs.
• Time to implement: Commonly 3-6 weeks for a first production workflow (faster for a pilot)
• Typical use cases: Inbound calls, Patient intake, appointment scheduling, outbound reactivation, post-visit follow-ups, basic patient questions.
• Differentiator: Healthcare-first positioning with practical, operations-oriented workflows and reporting.
• Relevant Success Story: Patient Reactivation - custom workflow that reactivated +1.5K patients.

My opinion

If you want a partner that will worry as much as you about patient workflows (and not just  “chat”), Puppeteer should be your go-to choice, especially for clinics and specialty practices that need value fast, with clean handoffs into the systems they already use.

Puppeteer takes a workflow-first approach, focusing on what needs to happen after a patient reaches out: how they are qualified, scheduled, followed up with, and routed when exceptions arise. The agents are designed to execute these steps end to end and to operate directly inside existing scheduling, EHR, and communication systems, rather than creating parallel processes.

To support this, Puppeteer begins with a structured workshop that serves as a practical entry point for organizations building patient-facing AI agents. The workshop is used to map current workflows, identify friction points and edge cases, define success metrics, and align on where automation delivers the most immediate value.

2. Hyro

Focus: Enterprise conversational AI for health systems (voice + digital front door)

Description: Hyro is built for healthcare organizations that need AI agents to handle large volumes across multiple channels like voice, chat, SMS, without breaking governance. They position their assistants as “responsible AI agents for healthcare,” and they’ve been visible in major health system use cases focused on patient access and communications. Hyro’s strength is usually breadth: once deployed, it can cover multiple departments and interaction types instead of solving a single narrow workflow.

• Compliance: HIPAA and SOC 2 (publicly referenced through healthcare positioning and third-party listings)
• Years of experience: Founded 2018 (≈7+ years)
• Type of client: Large health systems, enterprise provider groups
• Range of costs: Not public. Typically six figures to seven figures annually depending on scale, channels, and scope
• Time to implement: Often 3–6 months in enterprise environments
• Typical use cases: Access/scheduling, call deflection, FAQs, intake, routing to the right service line
• Differentiator: Enterprise readiness + real health system adoption stories.
  Relevant Success Story: Intermountain Health Achieves Multi-Digit ROI Using Conversational AI.

My opinion

Hyro is best understood as an enterprise communications platform rather than a workflow-specific AI agent builder. It works well for large health systems that already have mature governance, IT capacity, and long implementation timelines. For organizations seeking standardization across many departments, that can be an advantage.

That said, the tradeoff is complexity. Deployments tend to be slow, customization often requires significant internal effort, and meaningful value usually appears only after broad rollout. For teams looking for fast operational lift or narrowly defined healthcare workflows, Hyro may feel heavier than necessary.

3. SuperDial

Focus: Voice AI agents for revenue cycle calls (RCM)

Description: SuperDial is one of the clearest “this is what we do” companies on the list. Their product is aimed at automating time-consuming payer calls: eligibility checks, prior auth, claim status, credentialing, enrollment, work that usually burns staff time because of phone trees, hold times, and repetition. They also emphasize integration back into EHR/RCM workflows and being compliant for enterprise operations.

• Compliance: HIPAA & SOC 2 compliant
• Years of experience: Founded 2023 (≈2+ years)
• Type of client: RCM teams, billing companies, provider groups with heavy payer volume
• Range of costs: Not public. Often mid five figures to low six figures annually, depending on call volume and workflow coverage
• Time to implement: Commonly 4–8 weeks for initial workflows
• Typical use cases: Prior auth, eligibility, claims follow-up, payer navigation, documentation of outcomes.
• Differentiator: Deep specialization in payer phone workflows.
• Relevant Success Story: Scaling Provider Data Attestations with SuperDial at Orderly Health.

My opinion

If your readers are searching “best companies for outbound call agents using voice ai,” SuperDial is a strong candidate, because the use case is crisp and the ROI story is easy to measure. The tradeoff is scope: it’s great for RCM calls, but it’s not meant to be your broader patient access layer.

4. Hippocratic AI

Focus: Safety-oriented clinical support agents (non-diagnostic patient-facing work)

Description: Hippocratic AI positions itself around clinical safety and guardrails, targeting non-diagnostic patient-facing conversations and clinical support workloads. Public materials emphasize building a safety-focused model and carefully defining boundaries for what the agent should and should not do. The company was founded in 2023 and has attracted attention as one of the more clinically framed players in the “agentic AI” conversation.

• Compliance: HIPAA-oriented positioning; confirm BAA + data handling specifics during diligence.
• Years of experience: Founded 2023 (≈2+ years)
• Type of client: Health systems and clinical programs that prioritize safety, oversight, and governance
• Range of costs: Not public. Often high six figures+ in enterprise clinical contexts
• Time to implement: Typically 3–5 months, depending on approvals, escalation design, and clinical governance
• Typical use cases: Patient education, post-discharge follow-ups, care coordination, staff augmentation for non-diagnostic workflows
• Differentiator: Safety framing + clinical boundary discipline
• Clinical Case Study: Empathy in Action
  

My opinion

Hippocratic is compelling when your organization needs to defend the “why this is safe” story internally to clinical leadership, compliance, and risk committees. The tradeoff is speed: anything positioned around safety and clinical oversight tends to move slower, and you should expect tighter constraints on what the agent can do.

5. Kore.ai

Focus: Enterprise AI agent platform (horizontal) with healthcare solutions

Description: kore.ai is a broad enterprise conversational AI and agent platform (not healthcare-only), with healthcare-specific solutions layered on top. Their value is flexibility: orchestration, governance, multi-channel experiences, and enterprise controls. This is typically the kind of platform chosen by organizations that want to standardize across multiple use cases like patient access, member support, internal service desks, under one umbrella. Kore.ai has been around since 2014, which matters if you’re buying for longevity and roadmap stability.

• Compliance: Enterprise security posture; HIPAA is commonly referenced in their compliance/security content (verify exact scope for your deployment)
• Years of experience: Founded 2014 (≈11+ years)
• Type of client: Large enterprises (including healthcare), often with internal IT/AI teams
• Range of costs: Not public. Typically six figures to seven figures annually
• Time to implement: Often 4–6 months in enterprise settings
• Typical use cases: Contact center automation, patient/member support, internal agent workflows
• Differentiator: Platform depth + customization for complex enterprise environments
• Relevant Success Story: Florida Blue Offers Visual IVR Assistance to its Members

My opinion

Kore AI is a good fit when you need a configurable enterprise platform and you’re ready to invest in implementation. If you want “plug it in next month”-like service, it may feel heavy. If you want control and extensibility, it’s a serious contender.

6. Parakeet Health

Focus: AI-driven patient engagement for scheduling + outreach (voice and text)

Description:Parakeet Health centers on patient communications, automating repetitive contact center work like appointment scheduling and follow-ups, and connecting into EHR or practice management systems. Listings also describe referral outreach and care gap closure, which points to value-based and access optimization angles, not just “answer the phone.” Parakeet is newer (founded 2023), so the story is usually about speed and focus rather than long track record.

• Compliance: Sign BAAs with clients.
• Years of experience: Founded 2023 (≈2+ years)
• Type of client: Provider groups and health orgs optimizing access and outreach
• Range of costs: Not public. Likely mid five figures to low six figures annually, depending on volume and integrations
• Time to implement: Often 4–10 weeks, depending on scheduling/EHR integration
• Typical use cases: Scheduling/rescheduling, outbound outreach, follow-ups, backfilling, care gaps.
• Differentiator: Tight focus on patient access + engagement workflows (less “platform sprawl”)
• Relevant Success Story: Schweiger Dermatology and Aegis Medical Group Transform Patient Engagement with Generative AI Platform from Parakeet Health

My opinion

Parakeet reads like a “get real operational lift quickly” option, especially for access teams. The main tradeoff is maturity: with newer vendors, you want to be extra strict about security documentation, escalation behavior, and what happens when the agent fails mid-workflow.

7. EliseAI (HealthAI)

Focus: Patient communication automation across voice, SMS, email, and chat

Description: EliseAI’s healthcare offering is designed to manage the high-volume communication layer: inbound calls, scheduling, intake, reminders, and admin questions. A key detail is their omnichannel positioning (voice plus messaging channels) paired with integration claims across EHR/PMS/RCM systems. EliseAI explicitly states HIPAA and SOC 2 Type II compliance for healthcare, which helps reduce friction in procurement conversations.

• Compliance: HIPAA + SOC 2 Type II (stated by the company)
• Years of experience: Founded 2017 (≈8+ years)
• Type of client: High-volume practices and multi-location provider groups; teams prioritizing access operations
• Range of costs: Not public. Typically mid to high six figures annually for larger deployments
• Time to implement: Often 6–12 weeks for a first deployment; longer if multiple sites + deep integration
• Typical use cases: Scheduling, intake, reminders, patient messaging, billing questions
• Differentiator: Omnichannel patient comms + explicit compliance posture in their healthcare materials.
• Relevant Success Story: Women’s Health Connecticut Enhances Patient Access with Scheduling Automation Powered by EliseAI.

My opinion

EliseAI is a strong “front door automation” candidate when you want polished patient interactions and multi-channel consistency. The tradeoff is that these systems can drift into being a “new layer” if the integration and routing logic isn’t designed carefully, so the best outcomes come when teams treat it as an operational program, not just a tool.

8. Beeline Health

Focus: Referral scheduling and navigation (human-backed service)

Description: Beeline is a referral navigation service: clinics refer patients, and Beeline helps get the appointment scheduled handling obstacles, back-and-forth, and follow-up so the clinic’s team doesn’t get stuck playing phone tag. It’s clear from their site that the service is plan-sponsored and free to patients. That signals a focus on payer and provider partnerships rather than relying solely on per-seat SaaS.

• Compliance: Not prominently stated on the public marketing pages. If referrals involve PHI, confirm HIPAA/BAA handling in contracts
• Years of experience: Public profiles list 2024 founding for this Beeline category.
• Type of client: Primary care/referring offices; health plans that care about referral completion
• Range of costs: Public pricing shows a free tier for providers up to a referral volume threshold.
• Time to implement: Often days to a few weeks (because it’s service-led, not a deep platform rollout)
• Typical use cases: Referral scheduling, status tracking, reducing referral leakage, lowering admin burden.
• Differentiator: Human navigators + a workflow that’s designed around “referral completion,” not generic chat

My opinion

Beeline is interesting because it’s not trying to be “agentic AI for everything.” It’s trying to fix a specific operational gap with a service model. If your audience includes provider ops leaders, this is a practical option. The tradeoff is scalability and integration depth, you’ll want to understand how it plugs into existing referral systems and how reporting works over time.

9. Clearstep

Focus: AI triage + care navigation (voice and chat), plus scheduling optimization

Description: Clearstep is built around triage and routing, helping patients (and access teams) determine the right next step, then moving them into scheduling and documentation flows. They emphasize end-to-end intake, triage, routing/scheduling, plus integration with EHR and contact center systems. Clearstep also explicitly calls out enterprise-grade compliance: HIPAA, BAA support, and SOC 2. Founded in 2018, they’re one of the more established “triage + navigation” players in this list.

• Compliance: HIPAA, BAA, SOC 2 (stated on capability pages)
• Years of experience: Founded 2018 (≈7+ years)
• Type of client: Health systems, health plans, large access operations
• Range of costs: Not public. Typically six figures to seven figures annually for enterprise rollouts
• Time to implement: Often 2–4 months depending on protocols, routing, and integrations
• Typical use cases: Symptom triage, care navigation, voice triage, digital front door routing, scheduling optimization,
• Differentiator: Strong focus on triage + routing with enterprise compliance called out clearly
• Relevant Success Story: Duly Health & Care: 10x ROI with Virtual Triage.

My opinion

Clearstep is a strong option when your goal is “route correctly at scale” and reduce avoidable demand on clinical staff. The tradeoff is that triage systems live or die by protocol quality, escalation behavior, and integration into scheduling and care teams. If those pieces are aligned, the product category can deliver real impact; if not, it becomes another disconnected entry point.

Conclusion

In short:

Some of the companies we reviewed operate as large, enterprise platforms designed for scale, governance, and multi-department rollouts. Others focus on narrower, high-impact workflows like revenue cycle calls, referrals, or triage. And a smaller group functions more like custom builders or consulting partners, shaping AI agents around specific healthcare processes rather than asking teams to adapt to a fixed product.

Not all companies are solving the same problem, though. Some focus on enterprise-grade agentic AI designed for large health systems. Others operate more like consulting partners, building AI agents for small and mid-sized providers. Each model comes with different tradeoffs in cost, flexibility, speed, and long-term ownership.

There’s no single best AI agent company or best agentic AI company for every healthcare organization. The right choice depends on your size, risk tolerance, internal capabilities, and the problems you need to solve first, whether that’s patient access, outbound call automation, referrals, or care navigation. For teams evaluating AI agent developers or voice AI companies for outbound calling, the key signal is how well the agent integrates into everyday healthcare workflows and compliance requirements. 

Ultimately, AI agents work best when they’re treated as part of the care and operations team.They are not a replacement, and not a shortcut. The companies that succeed in this space are the ones building with that mindset in mind.