Vstorm

82% of clinical AI tools are built without clinicians as design partners. 🏥 That single fact explains most healthcare AI implementation failures. Before signing with any healthcare AI partner, ask when clinicians first shaped the solution, not reviewed it. We put together 8 questions that surface what a demo will not show: production track record, EHR integration depth, HIPAA governance, observability, and who owns the system after delivery. Full guide: 👇 https://lnkd.in/dzfgQkyJ

Most teams move a PoC to production because the demo looked good. That is not the same as production-ready. A production-grade AI agent needs three things a PoC almost never has: 📋 Evaluation datasets — so quality is measurable, not just felt 🛡️ Guardrails — because real users, unlike internal testers, will probe for weaknesses 📊 Observability — to detect failures at scale, not one conversation at a time The agent that impressed stakeholders and the agent your business can trust are built differently. Read the full breakdown (including why we use Logfire Evals from Pydantic) on the Vstorm blog. 🔗 https://lnkd.in/dv9QpRDu

70% of new POD customers need significant guidance before completing an order. At Mixam, that guidance was handled manually. Until it wasn't. We built a multi-agent order advisor that now handles 10,000 users daily. On day one in Australia: 11.76% more orders. 62% of quotes converted to paid. That is one use case. There are four more where AI process automation in print on demand removes manual overhead without adding headcount. We have written them up: current state, agentic solution, measurable results. Sounds interesting? Check the link below 👇 🔗 https://lnkd.in/dcR7Ji-i

Patient intake still runs on clipboards and phone calls at most practices. 50% of providers cite missing or inaccurate data as their #1 cause of claim denials — and most of that data is collected at intake. 📋 Agentic AI changes this. Not just digital forms — systems that read the full patient record, ask the right questions before the appointment, verify insurance via live API, and update the EHR automatically. We built this for a 100,000-member US healthcare provider, saving doctors 5+ hours a week. 🏥 Full breakdown below 👇 https://lnkd.in/dfWZYz3u

Building an AI agent for healthcare is not the same as building a random chatbot. 🏥 HIPAA compliance is not a feature you bolt on at the end. It is an architectural decision made on day one, with covering PHI handling, audit logging, access controls, and deployment topology. The MIT report shows 95% of enterprise AI pilots fail. In healthcare, the failure rate of non-compliant systems is effectively 100% — they never reach production. Our latest piece breaks down the architecture patterns and a deployment checklist for HIPAA-compliant AI agents. 🔒 Check the link below 🔗 https://lnkd.in/dqFnUEBf

78% of enterprise tech leaders have an AI pilot running. Only 14% have scaled one to production. The gap isn't the technology. It's the methodology. TriStorm — Vstorm's three-phase agentic AI implementation framework — was built specifically for this problem. Each phase feeds the next: discovery shapes the build, and every deployed agent generates operational data that sharpens the following use case. Simple before complex. Production evidence before assumptions. The full breakdown is in our latest article. 👇 🔗 https://lnkd.in/ds38wbES

Our friends from Pydantic shared our comparison of top 5 observability tools prepared by our engineering team, based on their experiences and day-to-day work. If you are choosing tech stack for your upcoming Agentic AI project - the text below is for you.

Prior authorization consumes 13 staff hours per physician, per week. 📋 That's not a resource problem. It's a workflow design problem. AI agents can now handle requirement checks, documentation assembly, submission, status monitoring, and denial drafting — autonomously. McKinsey estimates 50–75% of manual PA steps are automatable. One imaging network hit 98.5% accuracy on requirement determination alone. CMS-0057-F mandates FHIR-based PA APIs by January 2027. The compliance deadline is set. The question is no longer whether to automate. It's whether your team starts now or under pressure. https://lnkd.in/dGtV4vXH

Most POD platforms know their legal exposure is growing. ❓ Trademark disputes. ❓ VAT obligations across dozens of jurisdictions. ❓ AI-content disclosure requirements added mid-operation. ❓ EU distance selling laws that conflict with how POD returns actually work. The problem is not awareness. It is scale. No human review team catches derivative trademark infringement across 10,000 daily uploads. AI automation reduces average VAT filing time by 71% — from 42 to 12 hours per cycle. The same infrastructure that handles tax handles IP detection and content geo-restriction. We wrote about where it applies in POD specifically. Link below: ⚖️ https://lnkd.in/dYjHPpJA

We just published on the Pydantic blog. 🛠️ Production AI agents fail not because of bad models — they fail because simple agents cannot handle multi-step tasks, file operations, or error recovery at scale. That is why we built pydantic-deep — an open-source extension for Pydantic AI that brings deep agent capabilities: planning, sandboxed code execution, task delegation, and human-in-the-loop — with 100% test coverage. Read the full article 👇 https://lnkd.in/dh9B9fyt