The company’s plan includes AI-powered tools designed to streamline laboratory workflows and identify potential equipment problems before they disrupt testing.
Clinisys has unveiled an AI Innovation Roadmap designed to connect laboratory data, analytics, artificial intelligence agents, and workflow automation within the operational context of the laboratory. The open and governed architecture aims to support laboratories facing workforce, financial, technology, cyber, and data-governance pressures by delivering an operational intelligence layer that keeps human personnel in control.
According to the company, the roadmap advances the Clinisys Connected Laboratory Intelligence model through sequential phases, shifting facilities from systems of record and workflow to systems of intelligence and orchestration.
“Every laboratory decision, from the next sample to run to the next therapy to develop, should be informed by governed AI built on trusted laboratory data,” says Shay Hassidim, chief technology officer at Clinisys, in a release. “Our strategy is governed by design, grounded in laboratory context, and generative: agents that go beyond reporting by helping move work forward, with people in control.”
Dual Foundations for Laboratory Automation
The strategy establishes two pathways for laboratories to automate and expand workflows without locking organizations into a single proprietary AI ecosystem.
The first, CLS Model Context Protocol, is being developed to enable IT teams and advanced users to connect approved AI assistants to governed laboratory context. This setup provides controlled access to laboratory data and processes via CLS application programming interfaces (APIs), enforcing organizational permissions, rules, guardrails, audit trails, and observability.
The second, CLS NoCode AI, provides operational teams with reusable templates and connectors to configure governed automations. Target applications include reflex testing, result review, corrective and preventive action, and complaint handling. Both pathways build on the CLS Extended API and shared platform services, with plans for a Clinisys AI Marketplace to distribute certified agents, skills, and workflow templates across the ecosystem.
Core Initiatives and Agent Applications
Clinisys has organized the platform roadmap into five outcome areas: point-of-work copilots, advanced predictive and conversational analytics, workflow automation and orchestration, the AI Marketplace, and an Intelligent Lab Command Center designed to consolidate alerts, instrument monitoring, decision support, and agent supervision.
To demonstrate the platform in practice, the company detailed two initial agent applications:
- Analytics Agent: Guides authorized users through a structured conversational process to ask questions, visualize findings, analyze governed CLS data, export datasets, and schedule automated reports based on user permissions.
- Equipment Care Agent: Synthesizes instrument activity, quality-control metrics, maintenance schedules, service records, and workload assignments to support equipment-readiness workflows, aiming to catch instrument issues earlier and reduce avoidable reruns.
“The advantage is not a single agent,” says Prashant Prabudh, director, AI analytics and agentic platforms at Clinisys, in a release. “It is an open, governed intelligence layer where laboratory data, APIs, analytics, no-code workflows, and specialized agents can work together. That is how Clinisys advances from systems of record to systems of orchestration and creates a platform for continuous innovation.”
Specific product availability will be announced separately following readiness and approval reviews.
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