Tivara
Tivara is applying agentic architectures to healthcare, representing a seed vertical AI play with core generative AI integration.
As agentic architectures emerge as the dominant build pattern, Tivara is positioned to benefit from enterprise demand for autonomous workflow solutions. The timing aligns with broader market readiness for AI systems that can execute multi-step tasks without human intervention.
Tivara designs software to help doctors quickly automate prior authorization requests, so care is delivered faster.
Seamless, real-time AI-powered automation of patient phone workflows that integrates directly with major EMR/PMS systems and works on top of existing telephony infrastructure.
Agentic Architectures
Tivara uses autonomous AI agents to handle complex, multi-step patient communication tasks, including scheduling, intake, triage, and reminders. These agents interact with external tools (EMR, PMS) and execute workflows end-to-end.
Full workflow automation across legal, finance, and operations. Creates new category of "AI employees" that handle complex multi-step tasks.
Vertical Data Moats
Tivara leverages healthcare-specific data, workflows, and compliance requirements to build proprietary, domain-specialized AI agents. This creates a data moat based on medical practice operations and patient data.
Unlocks AI applications in regulated industries where generic models fail. Creates acquisition targets for incumbents.
Guardrail-as-LLM
Tivara implements compliance and safety guardrails, including HIPAA/SOC2 controls and escalation for low-confidence or out-of-scope requests, acting as a moderation layer on top of LLM outputs.
Accelerates AI deployment in compliance-heavy industries. Creates new category of AI safety tooling.
Micro-model Meshes
Multiple specialized agents are tailored to different medical workflows and specialties, suggesting a mesh of micro-models for task-specific automation.
Cost-effective AI deployment for mid-market. Creates opportunity for specialized model providers.
Tivara operates in a competitive landscape that includes Nabla Copilot, Syllable, Hyro.
Differentiation: Tivara emphasizes voice-based AI agents for phone calls, real-time EMR integration, and end-to-end automation of phone workflows, whereas Nabla focuses more on clinical documentation and digital assistants for providers.
Differentiation: Tivara highlights specialty-specific agents, rapid go-live (3-4 weeks), and deep integrations with a wide range of EMR/PMS systems, while Syllable is more focused on large health systems and may require more customization.
Differentiation: Tivara claims seamless integration with existing phone systems (no rip-and-replace), and workflow automation tailored to specialty practices, while Hyro often emphasizes omnichannel (web, SMS, phone) and larger enterprise deployments.
Tivara claims real-time integration with leading EMR and Practice Management Systems (Epic, Cerner, Athena, etc.) for direct scheduling, intake, and workflow automation. While many AI healthcare tools offer integrations, real-time, end-to-end workflow automation (e.g., booking directly into EMRs, creating new patient charts, surfacing requests in the patient chart) is technically challenging and less commonly achieved at scale.
The platform is designed to operate on top of existing telephony infrastructure, meaning clinics do not need to replace their phone systems. This is a pragmatic technical choice that reduces friction for adoption but requires robust middleware to bridge legacy telephony with modern AI agents.
Tivara emphasizes patient safety by default: AI agents escalate calls to humans when out-of-scope, low-confidence, or requiring clinical judgment. The escalation logic, if implemented robustly, is a non-trivial engineering and compliance challenge, especially in after-hours scenarios.
They claim enterprise agreements with AI model providers to ensure no patient data is used to train models. This is a defensibility signal, as it addresses a major compliance concern and may be a barrier for competitors using off-the-shelf LLM APIs.
The promise of going live in 3-4 weeks for end-to-end workflows suggests a highly templatized or modular integration layer, which, if true, is a significant technical achievement given the diversity of EMR/PM systems.
The product appears to be a thin layer over LLM APIs, with no mention of proprietary models or unique technology. The language focuses on 'AI agents' automating phone calls, but there is no detail on in-house NLP, speech, or agentic innovation.
The offering is highly focused on automating patient phone workflows, which is a single feature that could be easily absorbed by EHR/PM incumbents or telephony providers.
There is no clear data advantage or technical differentiation. Integration with EHRs is table stakes, and security claims (HIPAA/SOC2) are necessary but not unique. The product is easily replicable by others with access to LLM APIs.
Tivara's execution will test whether agentic architectures can deliver sustainable competitive advantage in healthcare. A successful outcome would validate the vertical AI thesis and likely trigger increased investment in similar plays. Incumbents in healthcare should monitor closely for early signs of customer adoption.
Source Evidence(15 quotes)
"Transform Patient Communication with AI Agents"
"Automate patient phone calls across scheduling, refills, intake, and after-hours workflows."
"24/7 patient engagement, powered by AI"
"Our AI resolves common requests autonomously, delivering faster answers and a better patient experience at scale."
"AI agents answer instantly, triage accurately, and keep your phone lines running 24/7."
"Fill cancellations and reduce abandoned calls with specialty specific agents trained on your practice's workflows."