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Use cases

One intelligence layer. Multiple data problems.

The same inferred model serves migration programs, integration work, analytics foundations, and AI systems that need enterprise context.

01

Data Modernization

Automatically map legacy schemas and dependencies before migration.

migration_readiness
legacy → discovered → target
Legacy schema
KNA1
VBAK
BSEG
Z_CUST_EXT
Discovered relationships
KNA1 → VBAK (1:n)
VBAK → BSEG (1:n)
Z_CUST_EXT ≈ KNA1
orphan keys · 3
Target architecture
dim_company
fct_order
fct_ledger_entry
dim_customer
02

Enterprise Data Integration

Understand how data from multiple systems fits together without manually mapping every relationship.

integration_map
4 systems → 1 entity model
Systems
CRM
ERP
Billing
Support
Resolution
identity key · email
company match · domain
conflicts · 12 flagged
merge rules · versioned
Unified entity model
Customer
Company
Subscription
Support Case
03

Analytics & BI

Give analytics teams a semantic understanding of enterprise data before dashboards and metrics are built.

semantic_resolution
question → entities → model
Business question
“Net revenue retention by region?”
Entities & relationships
Customer → Subscription
Subscription → Invoice
Company.region
Invoice.amount_total
Analytical model
metric: net_revenue_retention
grain: company × month
sources: billing, crm
04

AI & Agents

Provide AI systems with structured, contextual knowledge about enterprise data.

ai_context_layer
enterprise data → model → context
Enterprise data
postgres
snowflake
salesforce
kafka
SchemaPilot model
entities · 42
relationships · 318
semantic concepts · 87
lineage graph
AI context layer
schema context API
entity definitions
join paths
grounded query plans
Before / after

Replace data archaeology with data intelligence.

schema comparison
traditional ⟷ schemapilot
Traditional
Unknown schema—
Manual documentation—
Manual entity mapping—
Static lineage—
Repeated data discovery—
Constant maintenance—
With SchemaPilot
Automatically discovered schemas✓
AI-generated documentation✓
Resolved entities✓
Living lineage✓
Machine-readable relationships✓
Continuous updates✓
Trust & control

AI-assisted. Data-team controlled.

SchemaPilot is not an autonomous black box. Inferences arrive as reviewable suggestions with confidence, sources, and version history.

model governance
human-in-the-loop
AI suggestion
pending

Possible relationship detected

Source A
customer_id
crm.accounts
Source B
account_customer_id
erp.account_master
Confidence94%

Every inferred relationship remains explainable, traceable, and reviewable.

Who it is for

Built for the teams that own the data.

Data Platform

Build and maintain enterprise-wide models faster.

Analytics Engineering

Understand unfamiliar datasets without weeks of discovery.

CTO / Engineering

Reduce the complexity of integrating new systems.

AI Teams

Create structured context for enterprise AI applications.

Build the model your data is missing.

See how SchemaPilot can map your data environment.

Book a demo