Platform
An intelligence layer for enterprise data.
SchemaPilot sits between your data sources and the teams that depend on them.
read-only ingestionmetadata-firstversioned models
reference architecture
ingest → inference → interfaces
Data sources
PostgreSQL
Snowflake
Salesforce
SAP
Stripe
REST APIs
Kafka
CSV / Excel
SchemaPilot
Schema Discovery
Entity Resolution
Relationship Inference
Semantic Mapping
Data Lineage
Model Generation
Output
Unified Data Model
Data Catalog
Lineage Graph
Entity Graph
Analytics Layer
AI-ready Context
Platform capabilities
Six inference systems, one model.
Schema Discovery
Automatically understand unfamiliar databases and datasets.
›scan analytics.public · 412 tables
›classified keys · 1,180
›documented columns · 12,400
Entity Resolution
Identify when different systems are referring to the same real-world entity.
›crm.accounts ⟷ erp.KNA1
›match basis · domain + name
›clusters resolved · 96
Relationship Inference
Discover relationships that aren't explicitly defined in database schemas.
›fct_txn.inv_ref → Invoice
›coverage · 99.4%
›cardinality · n:1
Semantic Mapping
Understand that differently named fields may represent the same business concept.
›lifecycle_stage ← status
›amount_total ← gross_val
›concept · revenue
Data Lineage
Track how entities and attributes move across systems.
›stripe.charge → fct_txn → Invoice
›hops · 3
›freshness · 4m
Continuous Modeling
Keep the model synchronized as data infrastructure changes.
›watchers · 6 systems
›drift events · handled
›model version · v41
Trust & control
AI-assisted. Data-team controlled.
SchemaPilot proposes; your data team decides. Every inference carries confidence, source attribution, and an approval trail.
model governance
human-in-the-loop
AI suggestion
pendingPossible 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.