Integration surface
AI that works with the systems your enterprise already runs.
Enterprise AI rarely fails inside a model. It fails at the seams — between the system that holds the record, the one that holds the document, and the one where the work actually gets done. Integration is the architecture that closes those seams.
The problem
Every system is right about its own slice.
An enterprise runs on systems that were each designed to be authoritative about one thing and were never designed to be read together. The ERP knows the order, the CRM knows the relationship, the document store knows the terms, the warehouse knows the stock. Each is internally consistent and none can answer a question that crosses two of them — which is most of the questions worth asking. Integration in the sense that matters is not moving data between systems. It is building one layer that can reason across all of them and act back through them.
What integration usually means
A pipeline that copies records from one system to another on a schedule, and a dashboard that reports on the copy.
What it has to mean here
A governed layer where terms mean the same thing across systems, models can reason over it, agents can act through it, and the result is written back where the work lives.
What connects to what
Seven layers between a system of record and a decision.
This is the shape of every operational AI system Catalyze builds. Integration is not one of the layers — it is what holds them to the systems underneath.
- 01
Systems of record
ERP, CRM, ITSM, core banking, clinical and supply chain systems. The authoritative source, and the place the result has to land.
- 02
Data and semantic layer
Consolidation, validation, freshness — and one governed definition of every term, so the systems above can agree on what a word means.
- 03
AI and model layer
Models selected per task and routed, from private open-weight models inside your boundary through to a frontier model only where the task requires one.
- 04
Agents and business logic
Scoped agents with declared tools and permissions, and the deterministic rules they are not allowed to reason around.
- 05
Workflow orchestration
Triggers, approval gates, escalation paths and the handoffs between systems and between people. Designed for exceptions.
- 06
Enterprise applications
The result written back where the work happens, rather than into a separate dashboard nobody opens.
- 07
Human decisions
The judgement the system should not make alone, reaching the person with the authority to make it, with the evidence already assembled.
Selected integrations
Representative, not exhaustive.
A sample of the environments we have worked across, grouped by the role each plays in the architecture above.
Enterprise applications
Where work is assigned, tracked and closed.
- Salesforce
- ServiceNow
ERP and finance
Where the transaction and the ledger live.
- SAP
- Oracle
- NetSuite
Industry systems
The sector-specific platforms an operation actually runs on — and usually the hardest to integrate, because they were built for a workflow rather than for an API.
- Epic
- MEDITECH
- Experity
- nCino
- FIS
Data platforms and cloud
The substrate everything above it depends on.
- Snowflake
- Microsoft Azure
- AWS
- Google Cloud
Selected platforms we have integrated with. All marks are the property of their respective owners; their appearance does not imply partnership, endorsement, or certification.
How we approach it
Integration decisions we make early, because they are expensive later.
Read from the source
Reason against the system of record wherever latency allows, rather than against a copy that is already drifting.
Define before you connect
Agree what a term means across systems before building anything that depends on it. Skipping this is the single most common reason a programme stalls in month four.
Write back
A system that produces an insight but never changes state has not automated anything. Integration is bidirectional or it is reporting.
Permissions upstream
Entitlements enforced before the model sees data, not filtered out of its answer afterwards.
Assume the API is imperfect
Industry systems frequently have partial, rate-limited or undocumented interfaces. Designing for that from the start is cheaper than discovering it in integration testing.
Keep components replaceable
Models, and sometimes whole systems, will be swapped. Nothing should be welded to a vendor that will change.
Questions
What buyers ask us about integration.
What does Catalyze mean by integration?
Not a pipeline that copies records between systems on a schedule. We mean a governed layer where terms mean the same thing across systems, models can reason over it, scoped agents can act through it, and the result is written back into the application where the work happens. Integration in that sense is most of the engineering in an operational AI system.
Do you only work with the platforms listed here?
No. These are representative of environments we have worked across, not a catalogue of supported connectors. The architecture is designed to be source-agnostic, and most engagements involve at least one system that is specific to the organization.
Are these technology partnerships?
No. These are platforms we have integrated with. Their marks are the property of their owners and their appearance here does not imply a partnership, endorsement or certification of any kind.
What if our industry system has a poor API?
That is the normal case rather than the exception, particularly in healthcare and lending, where the systems were built around a workflow rather than an interface. It changes the integration design — more emphasis on document and event-level ingestion, careful rate handling, and reconciliation — but it rarely changes whether the work is possible.
Do you replace our existing systems?
No. Operational AI sits behind the systems of record and writes back to them. Replacing a core platform is a different and far riskier programme, and it is almost never what the operational problem actually requires.
Tell us which systems you run. We'll tell you where the seams are.