Operations run on data nobody can see in time.
Healthcare organizations measure everything and can act on almost none of it while it still matters. Catalyze builds the governed layer underneath, then the executive intelligence and agent workforce on top of it.
The operating reality
The data exists. The answer arrives three weeks late.
A healthcare operation generates an enormous amount of operational signal and almost none of it is available when a decision is being made. The practice-management system, the clinical record, document intake and telephony each hold part of the picture, and assembling them is a manual job — which means it happens on a reporting schedule rather than when someone needs it.
- Operational data split across practice management, clinical records, intake and telephony
- The same metric defined differently in two reports, both defensible
- Network and site KPIs assembled by hand into recurring decks
- Questions that arise between reporting cycles simply going unanswered
- Compliance exposure visible only when somebody goes looking for it
- High-volume repetitive work consuming clinical and revenue-cycle staff
Where we work
The operational problems that respond to a governed data layer and scoped agents.
01
Executive intelligence
A daily operating picture generated rather than assembled, with recommended actions carrying an owner and a timeframe.
02
Natural-language analysis
An operator asks a question and gets an answer with its sources, freshness and caveats attached.
03
Revenue cycle
Eligibility, benefit activation, documentation completeness and claim exceptions handled as workflows.
04
Patient data integrity
Duplicate record resolution and intake completeness — the quality everything downstream depends on.
05
Workflow automation
Inbound document classification and routing, and the repetitive processing behind the front desk.
06
Workforce and compliance
Coverage, credentialing and training status surfaced continuously instead of on discovery.
Architecture
What the system looks like in a health system
One governed foundation, forking into the two things different people need from it.
- 01
Source systems
Practice management / EMR · clinical · intake · telephony
The operational record, already spread across four categories of system that were never designed to be read together.
- 02
Data platform
Warehouse · automated ingestion · quality checks
Consolidation with validation at the boundary and freshness tracked, so the age of an answer is always knowable.
- 03
Semantic layer
Governed metrics · site hierarchy · comparison windows
One agreed definition per operational metric. This is what stops two reports disagreeing about the same week, and it is the layer that has to exist before any AI surface is trustworthy.
- 04
AI layer
Text-to-SQL over governed metrics · narrative synthesis
Language over the semantic layer, never over raw tables — so the system cannot invent a definition it was not given.
- 05
Executive intelligence
Daily brief · site spotlights · ranked actions
The operating picture, generated on a schedule and on demand, with provenance and uncertainty stated as output rather than buried.
- 06
Agent workforce
Document routing · record resolution · benefit activation
Scoped agents clearing the highest-volume repetitive workflows, each measured on what it completed and the time it returned.
- 07
Governance
Role-based access · audit trail · stated baselines
Who can ask what, enforced before the model sees anything, with assistant conversations retained and reviewable. An AI interface without an audit trail is not deployable in a clinical setting.
- 08
Operators
Executives · regional managers · site leads · revenue cycle
The people the system exists to give time and judgement back to.
Evidence
What we have built here
One enterprise implementation and one platform, and they are different things.
Stated plainly
These are deliberately distinct. The urgent care work is evidence of Catalyze implementing operational AI inside a real enterprise healthcare environment. Claritus.One is the broader AI-native platform direction. Neither is presented as the other.
Capabilities that apply
What we would bring to healthcare.
Related insights
Our thinking on this.
Questions
What buyers ask us about healthcare.
How can healthcare organizations use AI operationally?
The durable applications are not clinical decision support — they are the operational layer around care: consolidating fragmented data into governed metrics, generating the operating picture leadership currently assembles by hand, answering questions between reporting cycles, and clearing high-volume repetitive work like document routing, duplicate record resolution and benefit activation.
Why does healthcare AI need a semantic layer first?
Because the same metric is usually defined three different ways across three systems, and an AI surface built on top of that will produce answers that contradict last week's report. One governed definition per metric, agreed before anything is put in front of an executive, is what makes the output trustworthy. It is unglamorous and it is the thing that determines whether the programme survives.
How is patient data protected?
Through role-based access enforced before the model receives anything, a routing architecture that keeps most processing inside the organization's boundary, and retained, auditable records of every assistant interaction. These are architectural controls that reduce exposure and make it reviewable — they are described here as design, not as a compliance certification.
What is the difference between your healthcare work and Claritus.One?
The urgent care engagement is a system Catalyze designed and built inside a specific client's operation, described with the client anonymized. Claritus.One is a Catalyze platform: the same architectural pattern built once as a product so an organization does not have to rebuild the foundation before it can automate anything.
Tell us about the operation. We'll tell you whether we have built something like it.