Operations
PatternDocument intake and extraction
Contracts, statements and forms parsed into the canonical model with provenance retained per field.
Give the machine your vocabulary.
AI applied to ungoverned data produces confident answers to the wrong question. The semantic layer is where your definitions, entitlements and lineage live — the thing that makes model output about your business rather than about your documents.
The business problem
The model is not the bottleneck. The definitions are.
Most enterprises can produce the data. What they cannot produce is one agreed definition of the entity the data describes, with the access rules and lineage attached. Without that, every AI component re-derives its own interpretation, and the answers diverge in ways nobody can trace.
What that looks like
- The same metric computed three ways in three systems
- No lineage, so an output cannot be traced back to its source records
- Entitlements enforced in the UI but not in the data the model sees
- Definitions living in analysts' heads rather than in the platform
What Catalyze builds
What we build
The layer between systems of record and everything that reasons over them.
- 01
Entity and metric definitions
One canonical definition per business entity and measure, versioned and owned, used by every downstream component.
- 02
Lineage and provenance
Every derived value traceable to source records, so an answer can be defended rather than just produced.
- 03
Entitlement enforcement
Access rules applied at the data layer, so a model or agent cannot see what the requesting user could not.
- 04
Retrieval design
Chunking, indexing and retrieval built around the actual shape of your documents and the questions asked of them.
- 05
Data quality gates
Validation at ingest, so malformed or stale input is caught before it reaches a decision path.
Architecture
From records to meaning
What sits between a system of record and a model that has to be right.
01Source Systems
Systems of record, document stores, operational databases and third-party feeds.
02Ingest + Validation
Batch and change-data-capture pipelines with schema and quality checks at the boundary.
03Canonical Model
One definition per entity and measure, versioned, owned and documented.
04Lineage + Provenance
Every derived value traceable back to the records it came from.
05Entitlements
Row, column and document-level access enforced here rather than in the interface.
06Retrieval + Indexing
Structured queries and document retrieval shaped around the real question set.
07Consumers
Models, agents, orchestration and analytics, all reading the same definitions.
Example use cases
Where this applies
Patterns we design for. Items marked as published link to our own write-up.
Enterprise
PatternGoverned retrieval for internal assistants
Retrieval that respects entitlements, so an assistant answers from what the user is allowed to see.
Technology & integration
Integration surface
Described at category level except where Catalyze publicly names a platform.
Named platforms
- Salesforce
- Workday
- ServiceNow
- AWS
Sources
- ERP / CRM / ITSM
- Core banking
- Document stores
- Third-party feeds
Pipeline
- Change data capture
- Batch orchestration
- Schema validation
- Quality gates
Serving
- Warehouse / lakehouse
- Vector index
- Metrics layer
- Lineage catalog
Case studies
Proof in production.
Related insights
Our thinking on this.
Questions
What buyers ask us about Data & Semantic Layer.
Do we need to finish our data programme before doing anything with AI?
No, and waiting is usually the wrong call. We scope the semantic layer to the workflow being automated rather than to the whole estate — one domain, defined properly, is enough to put a system into production and it compounds from there.
Is this the same as a data warehouse project?
It overlaps but it is narrower and more opinionated. A warehouse consolidates data; the semantic layer fixes what the data means, who may see it, and where it came from. Those are the properties an AI component needs and a warehouse alone does not guarantee.
How do entitlements work when a model is involved?
They are enforced at the data layer, before retrieval. A model or agent operating on behalf of a user can only reach records that user is entitled to, which keeps access control out of the prompt where it cannot be relied on.
What about unstructured documents?
Documents are ingested into the same canonical model with provenance retained per extracted field, so a value pulled from a PDF is as traceable as one read from a database.
Tell us about the workflow. We'll tell you whether Data & Semantic Layer is the right place to start.