CognitiveMed AI unifies fragmented health systems, automates clinical and operational workflows, and layers agentic AI across the journey — moving organizations from describing what happened to prescribing what to do next.
Every layer is purpose-built for clinical environments — from the data standards underneath to the AI apps your own teams build on top.
Standards-based clinical data interoperability across every connected system.
Variant interpretation and precision medicine intelligence.
Clinical NLP, semantic understanding, and auto-coding.
Modular clinical and operational applications.
Search by meaning, context, and similarity.
No-code creation of healthcare AI solutions.
Most health systems are stuck describing the past. CognitiveMed AI moves you up the analytics maturity curve — pairing data mining, process mining, and agentic AI to make operations proactive rather than reactive.
Integrated dashboards and historical analytics give leaders a clear, evidence-based view of operations and clinical history.
Process mining reconstructs real clinical and operational pathways from the data itself, surfacing the root causes behind outcomes.
Internally and externally developed models forecast risk, deterioration, and demand — embedded directly in the workflows that act on them.
Agentic AI and automation close the loop — recommending and executing the next best action. Proactive, not reactive.
We pioneer novel analytical methods by combining three disciplines into one engine — advanced analytics, intelligent decision-making, and autonomous process execution working as a single system.
Data mining and process mining extract patterns, pathways, and cohorts from the raw signal — turning fragmented records into structured intelligence.
Predictive, survival, vector, and generative models layer over your data — embedding clinically defensible intelligence at the point of decision.
Agentic AI lets systems talk to each other and replace manual, repetitive tasks — autonomously executing the next best action for efficiency and a durable competitive edge.
The same underlying engine powers research, care delivery, and operations — deep domain knowledge and best practices, delivered cost-effectively and at lower risk.
Define and discover patient cohorts across structured and unstructured data for research and care.
Discover and optimize real care pathways reconstructed directly from event data.
Design and develop study protocols grounded in real-world population evidence.
Build and validate new predictive and diagnostic signals with integrated MLOps.
Rapid "look-see" feasibility queries before committing to a full study or build.
Match patients to analogous cohorts using genomic, clinical, and social similarity.
Orchestrate clinical and administrative workflows end to end with agents and automation.
Monitor sensor and environmental streams and act on them in real time.
Forecast capacity, volume, and resource needs across the system.
Optimize inventory, supply chain, and resource flow with operations research and demand-driven automation.
Surface temporal and stationary anomalies before they become incidents.
Track quality-control parameters and detect out-of-trend behavior automatically.
CognitiveMed AI consolidates diverse clinical, genomic, and operational datasets into a single, standards-based foundation — automatically fetching and merging related information from wherever it lives.
From schema-aware querying to managed model lifecycles, the platform takes intelligence from exploration to dependable, production-grade operation.
Reconstruct clinical pathways directly from event logs — a continuous self-improvement engine that reveals how care actually happens versus how it was designed.
Run predictive diagnostic and survival analyses with both internally and externally developed models, including interactive what-if scenario controls.
Manage the full machine-learning lifecycle in production — versioning, monitoring, and performance evaluation so models stay accurate and accountable over time.
Cluster populations by genomics, family history, and social determinants — then match patients to analogous cohorts to anticipate likely treatment outcomes.
CognitiveMed AI puts agents where healthcare actually needs them: inside the governed workflows that run the enterprise, not bolted on beside them.
It turns the question a clinician actually asks into a query you can run. Most cohort tools stop at what happened — a flat list of conditions, medications, and observations. Our agent asks when and where, relative to everything else in the record.
A stroke, then a new medication one to three months later, then a follow-up within five months of that. Care episodes that began within 50 km of home. Time-to-treatment measured against the event that actually governed it — not an average of everything nearby.
Every step is a node; every edge carries a temporal or spatial window — so the same resource can appear more than once in a chain.
Runs on standard FHIR · no new data model · no migrationTeams visualize their automation as a graph, then drop in an agent node — an AI reasoning step that decides for itself which sources to consult and returns a structured, evidence-cited proposal.
FHIR records, DICOM imaging, databases, knowledge graphs, trained predictors and detectors — even live traffic, weather, GIS, and logistics.
One reasoning engine, every system you run on — triggered on demand, on schedule, or by live events.
Agents hold read-only access by construction. Every consequential action follows a propose → approve → execute path with a clinician's decision in the middle — and the graph itself refuses to be drawn any other way.
The agent observes and drafts a recommendation.
A human reviews the evidence and decides.
Clinician in the middleOnly an approved action is ever carried out.
Agents can observe and propose — never act on their own.
What the agent saw, what it proposed, why, and who approved.
BAA'd cloud or fully on-premises inference — PHI never leaves.
No model reaches production without passing the workflow engine.
CognitiveMed extends beyond analytics with AI-native, agent-based applications built on the underlying data platform, AI services, and workflow automation. Each one continuously monitors data, reasons over changing clinical and operational conditions, coordinates workflows, surfaces recommendations, and initiates action — with human oversight where it's required.
Every application reads the same FHIR resources — no app owns its own databaseInterprets genomic information alongside clinical and phenotypic data to power variant analysis, patient stratification, biomarker discovery, and personalized treatment — advancing precision medicine and research.
Optimizes OR utilization through intelligent scheduling and resource coordination — coordinating personnel and equipment, anticipating scheduling conflicts, and improving surgical throughput and efficiency.
Streamlines study management, patient and cohort identification, recruitment, and protocol compliance — integrating clinical and research data to accelerate study execution with greater operational visibility.
Monitors and automates clinical-trial and commercial-scale manufacturing — coordinating production workflows, tracking quality parameters, flagging out-of-trend conditions, and delivering real-time supply-chain intelligence.
Automates the revenue cycle end to end — from patient access and eligibility to coding, claims, denials, and collections. Agents surface revenue leakage, prioritize exceptions, and automate follow-up to accelerate cash flow.
Build, storyboard, and manage a portfolio of data-driven applications — activated however your environment demands.
Built-in dashboards, portals, and mobile apps put intelligence directly in users' hands.
Observe IoT, Kafka, and database streams and react the instant conditions change.
Run periodic, automated processing and monitoring with no human in the loop.
Trigger and integrate via API from EHRs and downstream consumer applications.
See how CognitiveMed AI adapts to your environment — Epic, Cerner, or beyond — and turns your clinical data into measurable outcomes.