01
Clinical triage guideline retrieval
An agent reads proprietary triage protocols and returns a sourced, auditable recommendation — not a generic AI guess.
Agentic AI in healthcare
Agents that work inside clinical operations .
We build agents that read clinical guidelines, verify claims, and coordinate patient touchpoints — with the audit trail and human review a clinical or compliance team will actually sign off on.
Trusted by operators
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Most healthcare AI stops at the pilot
Triage guidelines, claims documentation and patient intake all turn on unstructured input and a high cost of being wrong. Models read that input well enough. What keeps a healthcare pilot from reaching production is the machinery around it: an evaluation harness, reasoning traceable to a source, and a human-in-the-loop gate that a clinical or compliance review will accept.
every recommendation traces to a sourced guideline
Use cases
Where agents earn trust in healthcare operations
Workflows with a clear decision boundary and a paper trail already required.
02
Claims verification
Cross-checks accident and claim details against multiple internal systems before a human signs off — hours of manual review compressed to minutes.
03
Multi-channel patient scheduling
An SMS and voice agent gathers pre-visit information and coordinates availability — reducing administrative load per physician.
04
Care coordination follow-up
Post-discharge check-ins flag risk signals and route to staff — continuity of care without a manual call list.
The cost of manual review
Where clinical and claims workflows lose hours today
These numbers come from real shipped agentic AI engagements. Schmitt-Thompson's is a direct healthcare deployment; the ARIJ Network and Mixam figures are not healthcare — they show the same before/after validation and orchestration mechanism in media and retail. Every figure below links to the case study behind it.
Agents do not make unsupervised clinical judgments. They compress the gathering, cross-checking and drafting that sits before every decision. Each step is logged for review.
Knowledge-inquiry response rate before the agent
1%
Not a healthcare deployment — ARIJ Network's Moodle knowledge base went largely unanswered outside a narrow set of scripted replies.
0
Hallucination events across 329+ clinical scenarios
STCC's triage guideline agent was validated scenario by scenario before clinical use — every recommendation traces to a sourced guideline.
95.4%
Success rate for a production multi-agent advisor
Not a healthcare deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
Client results
Proof from production healthcare deployments
Schmitt-Thompson is our direct healthcare proof. The other cases are outside healthcare — they show the same sourced, escalation-gated agent mechanism in media, commerce and engineering.
“The team at Vstorm was very helpful, their insight and experience helped us greatly in our project. They were very professional at every step of the way and made the whole process feel seamless.”
1% → 100%
Knowledge-inquiry response rate before and after
Synera · Workflow generation
Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass.
2 hrs → 3 min
to generate a validated workflow
Delivery path
From workflow audit to a production clinical agent
TriStorm keeps clinical validation and engineering aligned — safety risks surfaced before full build commitment.
Map the process and data constraints
We audit the target process, HIPAA and data-access constraints, and existing system boundaries — ranking use cases by clinical impact and implementation risk.
- Workflow & compliance audit
- Data access map
- Prioritised use case
Build and validate against guidelines
We implement against real clinical data shapes, with an evaluation suite scored against your own guidelines before any output reaches a clinician.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with clinical oversight and support
Production rollout with monitoring, audit logging, and a structured handoff so your clinical and compliance teams operate the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own clinical and claims workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in healthcare, answered
What is agentic AI in healthcare?
Agentic AI in healthcare is software that plans and carries out a multi-step clinical or administrative task across systems (retrieving guidelines, cross-checking records, and drafting an output) with a clinician or reviewer as the final gate. Unlike a chatbot that answers one message at a time, an agent completes work such as a triage guideline lookup or a claims verification, and escalates when its confidence drops. It does not make unsupervised clinical judgments; it gathers, cross-checks, and drafts for human review.
Where does agentic AI actually fit into clinical operations?
In workflows with a clear decision boundary and a paper trail already required — triage guideline lookup, claims verification, pre-visit intake, care coordination follow-ups. We do not deploy agents to make unsupervised clinical judgments; we deploy them to gather, cross-check, and draft, with a clinician or reviewer as the final gate.
How is this different from a generic AI chatbot bolted onto our patient portal?
A chatbot answers one message at a time. An agent reasons across multiple sources (guidelines, records, claims data) completes a multi-step task, and escalates when confidence drops. Our clinical triage build for Schmitt-Thompson Clinical Content validated 329+ scenarios before going live, with zero hallucination events across the full set.
How do you handle HIPAA and patient data?
Agents integrate through secure, HL7-compliant interfaces and operate within your existing access controls — role-based permissions, encryption, and audit logging on every action. We design the data path before we design the agent.
What happens when the agent is not confident in its answer?
It escalates. Confidence thresholds and guideline coverage gaps route to a human reviewer with the reasoning attached — the same pattern we built for STCC's triage system, where every recommendation traces back to a sourced guideline.
Can this integrate with our existing EHR and scheduling systems?
Yes, through your existing APIs and data infrastructure, not a rip-and-replace. That is the same integration discipline behind every production agent we have shipped — for ARIJ Network, for example, the agent reads and answers only from their existing Moodle environment without touching the underlying platform.
What is the typical timeline to a working system?
A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks. Full production rollout with monitoring and clinician handoff follows the same TriStorm phases as any other Vstorm engagement.
What have agents actually delivered in healthcare deployments you have shipped?
For Schmitt-Thompson Clinical Content, a zero-hallucination triage guideline agent was validated across 329+ scenarios before going into clinical use — a real, named healthcare deployment, not a projection. Our other production agents shown below are outside healthcare (ARIJ Network, Mixam), but demonstrate the same sourced, escalation-gated mechanism applied to multilingual support and commerce workflows.
What does zero hallucination across 329+ scenarios mean for a clinical deployment?
For Schmitt-Thompson Clinical Content, we validated the triage guideline agent scenario by scenario (329+ cases) before it went into clinical use, with no hallucination events recorded across the full set. Every recommendation traces back to a sourced guideline, and any case outside guideline coverage escalates to a human reviewer rather than being answered by the agent.
Start with one workflow
Map one clinical or claims workflow worth automating
A 30-minute call identifies compliance constraints, integration points, and a realistic path to a working agent your clinical team will trust.