Claritus.One: a shared operational AI layer for healthcare organizations
Every healthcare operator we have worked with rebuilds the same foundation before it can automate anything: consolidate the data, agree what the metrics mean, then attach the agents. Claritus.One is that foundation built once as a product — a shared data and semantic layer with operational agents across scheduling, revenue cycle, patient data integrity and staffing.
Platform
Claritus.OneContext
The expensive part of operational AI is not the AI.
The pattern repeats across healthcare organizations. The systems of record are fragmented, the same metric is defined three different ways in three different reports, and so any attempt to automate a workflow starts with six months of data and definition work before a single agent does anything useful. That foundation is substantially the same from one operator to the next, and rebuilding it per engagement is a tax on every project that follows.
Challenge
Point solutions cannot share what they each need most.
The usual response is a set of point tools — one for scheduling, one for the revenue cycle, one for staffing. Each arrives with its own partial copy of the data and its own private definition of the metrics, which means they cannot reason about each other. A scheduling change that creates a staffing problem that creates a revenue problem is one situation, and three disconnected tools will see three unrelated events.
- Fragmented systems of record, with no shared operational view across them
- The same metric defined differently in every tool that reports it
- Point solutions that each rebuild a partial version of the same foundation
- Automation projects that spend most of their budget before automating anything
- No path from a workflow that works in one department to the rest of the organization
Approach
Build the layer once, then attach agents to it.
Claritus.One inverts the usual order. Rather than a tool per department, it establishes the shared layer first — one consolidated operational data set with one governed definition per metric — and then treats each operational domain as an agent attached to that layer. Because every agent reasons over the same definitions, they can reason about each other, and a workflow proven in one domain does not have to be rebuilt to reach the next.
System
What the platform provides
A foundation, a set of domain agents on top of it, and a role-appropriate way in.
01
Shared operational data layer
Consolidation across systems of record, with ingestion, validation and freshness tracking handled once rather than per tool.
02
Governed semantic layer
One agreed definition per operational metric, with hierarchy and comparison windows encoded, so every surface above it reports the same number.
03
Domain agents
Operational agents scoped to scheduling, revenue cycle, patient data integrity and staffing — each narrow enough to be accountable and each reasoning over the same shared definitions.
04
Role-based experience
An executive, a department lead and a coordinator need different things from the same system. What each role sees and can ask is scoped to what that role is accountable for.
05
Provenance by default
Every figure and every recommendation carries its source, its freshness and the assumptions behind it. The same discipline applied in our client work, built into the product.
06
Audit and governance
Role-based permissions over queries and actions, with agent activity and assistant conversations retained for review.
Architecture
The system
A single spine that fans out into domain agents and converges on the people accountable for the work.
- 01
Source Systems
Whatever the organization already runs on. Claritus.One is designed to sit behind these, not replace them.
- Practice management / EMR
- Scheduling systems
- Billing + claims
- HR + workforce systems
- Document intake
- 02
Data Platform
Consolidation, validation and freshness — built once for every agent above it.
- Automated ingestion
- Warehouse
- Validation at boundary
- Freshness tracking
- 03
Semantic Layer
The shared vocabulary. This is what allows agents in different domains to reason about the same situation.
- Governed metric definitions
- Organizational hierarchy
- Comparison windows
- Threshold + target registry
- 04
AI / Model Layer
Reasoning over governed definitions, with routing by task rather than one model for everything.
- Text-to-SQL over the semantic layer
- Narrative synthesis
- Classification + extraction
- Anomaly + threshold detection
- 05
Domain Agents
splits into 4The fan-out. Each domain is a scoped agent rather than a separate product with its own private data.
Scheduling
Capacity against demand, and the consequences of changing either.
- Demand patterns
- Capacity analysis
- Schedule exceptions
Revenue Cycle
The path from visit to payment, and where it breaks.
- Eligibility + benefits
- Claim exceptions
- Documentation completeness
Patient Data Integrity
The data quality everything downstream depends on.
- Duplicate resolution
- Record reconciliation
- Intake completeness
Staffing
Coverage, compliance and the cost of both.
- Coverage modelling
- Credential + training status
- Utilization
- 06
Orchestration
Where agent output becomes something that actually happens, with a human in the loop where it should be.
- Escalation routing
- Approval gates
- Action tracking
- Audit trail
- 07
Roles
Scoped to accountability rather than to seniority.
- Executives
- Department leads
- Coordinators
- Compliance
Implementation
How it deploys
Claritus.One is designed to be adopted in the order that builds trust, not the order that demos best.
Technologies
Data
- Automated ingestion
- Warehouse
- Semantic layer
- Validation
AI layer
- Text-to-SQL
- Model routing
- Classification
- Narrative synthesis
Workflow
- Agent orchestration
- Approval gates
- Escalation routing
- Audit trail
Access
- Role-based permissions
- Scoped querying
- Activity logging
- 01
Connect
Systems of record connected and consolidated, with validation and freshness tracking in place before anything is reported.
- 02
Agree the definitions
Metric definitions, hierarchy and comparison windows encoded into the semantic layer. Contentious definitions get settled here, in the open.
- 03
Report
Reporting and natural-language analysis first, because it is the cheapest way to prove the foundation is correct before anything acts on it.
- 04
Attach agents
Domain agents enabled one at a time, each scoped narrowly and measured on its own workflow rather than on platform-wide claims.
- 05
Extend
A proven workflow extends to the next domain without rebuilding the layer underneath it — which is the entire reason the layer exists.
Outcome
What the platform is built to change
Claritus.One exists to remove the foundation work from the front of every operational AI project. The intent is that an organization spends its effort on the workflows that matter to it rather than on rebuilding a data and semantic layer that looks much the same everywhere.
- Foundation built once rather than rebuilt per department or per project
- One agreed definition per metric across every surface that reports it
- Agents in different domains able to reason about the same situation
- Shorter path from a proven workflow to the next domain
- Provenance and audit present from the first deployment rather than added under pressure
Results
Claritus.One is a Catalyze Labs platform. Deployment outcomes depend heavily on an organization's existing systems and data quality, and are covered by client confidentiality where they exist. We share specifics directly, under NDA, rather than publishing figures that would not transfer.
Capabilities involved
What this system is made of.
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