From scattered enterprise data to governed intelligence APIs

A diversified enterprise has valuable customer, product, service and commercial data distributed across operational systems, warehouses and files. ASE creates a semantic access layer and a catalog of governed APIs while preserving the existing systems of record.

Example project. The company, systems and figures are made up.

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The situation

Every new application, report or AI assistant triggers another bespoke integration. Ownership of a field is unclear, sensitivity classification is inconsistent, and each consumer re-implements its own joins against sources that keep changing.

The request

Let applications, employees and authorized AI agents discover and retrieve trusted enterprise data through consistent APIs without centralizing every source again.

Systems involved

Salesforce
SAP
Snowflake
Databricks
ServiceNow
Product information management
SharePoint and managed files
Inconsistent REST and event interfaces

How it runs

Step by step

What ASE does at each stage and who approves it.

DiscoverHuman approval gate

Inputs

Source schemas, catalogs and existing APIs
Transformation logic and lineage metadata
Data policies, glossaries and usage telemetry

Agent activity

Analyze schemas, catalogs, APIs, transformations and lineage
Classify sensitive data and infer ownership
Assemble a data-product backlog from observed demand

Human authority

Data owners validate authority, sensitivity and permitted use.

Approval criteria: Every in-scope source has a named owner and a confirmed sensitivity class.

Outputs

Source inventory
Ownership model
Sensitive-data classification
Lineage graph and data-product backlog

Traceability created

Each field links to its source system, owner and classification

Hand-off: Validated inventory scopes the first data products.

Output

What ASE produced

Sample outputs from this project. All data is made up.

Source graph

Federated source map with ownership and sensitivity classification.

SALESFORCE account, contact, opportunity owner: Commercial Ops
SAP customer master, orders, billing owner: Finance Systems
SNOWFLAKE revenue marts, trailing metrics owner: Enterprise Data
SERVICENOW service cases, entitlements owner: Service Ops
PIM product hierarchy, availability owner: Product Data
classified fields: 214 (restricted 38 · confidential 96 · internal 80)

Approvals

Where people decide

Discover
Validate authority, sensitivity and permitted use
Approved by: Data owners
Define
Approve semantics, contracts and access
Approved by: Domain owners
Validate
Approve quality, lineage and security evidence
Approved by: Data stewards and security
Deploy
Authorize the controlled consumer group
Approved by: Platform owner

Solution

What got built

Target architecture

CONSUMERS applications · employees · authorized AI agents
GATEWAY workload identity · entitlement · quota · masking · versioning
SEMANTIC canonical entities · data contracts · freshness and quality rules
ADAPTERS Salesforce · SAP · Snowflake · Databricks · ServiceNow · PIM · files
GOVERNANCE classification · lineage · policy denial audit · steward approval

How it traces back

Glossary term → contract v1.0 → source field → quality rule → TC-DIF-042 → API v1 release

Targets

What a pilot would test

Goals to prove in a pilot, not client results.

Recurring interface provisioning
Illustrative baseline 6–10 weeks
Modeled pilot target 2–4 weeks
Target range depends on system and process complexity
Reusable governed data products
Modeled pilot target 3–5
Potential outcome to validate
Source lineage
Modeled pilot target 100% for in-scope API fields
Potential outcome to validate
Automated quality rules
Modeled pilot target 20–30 critical rules
Target range depends on system and process complexity