No more custom mappings. Transform incompatible schemas into unified, reusable models to create a consistent, scalable data foundation across systems and partners.
For a quick look at the ideas behind Data Nexus Schema normalization and mapping, watch the short video.
Schema Normalization and Intelligent Mapping provide a consistent data foundation across systems and partners. Data Nexus transforms incompatible data models into unified schemas, enabling clean, predictable data exchange without custom point-to-point integrations.
Most integrations fail not because systems can’t connect—but because their data doesn’t match.
Different systems use different field names, structures, formats, and assumptions. Every new partner introduces custom mappings, brittle logic, and long onboarding cycles. Over time, integrations become expensive to maintain and impossible to scale.
Partner onboarding takes weeks instead of days
Small schema changes break downstream systems
Data quality issues propagate across integrations
Engineering teams become bottlenecks for growth
Data Nexus eliminates schema chaos by standardizing data once and reusing it across every integration.
A unified schema layer powered by intelligent automation to eliminate data inconsistency across integrations.
Standardizes disparate data models into unified schemas
Normalizes field names, structures, and data types across systems
Applies transformations for structural, semantic, and format alignment
Leverages an AI Mapping Agent to suggest and accelerate schema mappings
Enables reusable mappings across partners and integrations
Decouples producers and consumers from each other’s schemas
Reduces manual effort while preserving human control
A structured normalization pipeline enhanced by an AI agent that learns, adapts, and assists as schemas evolve.
Ingests schemas and data from source systems
Aligns incoming data to a unified canonical schema
Uses an AI Mapping Agent to detect, recommend, and refine mappings
Aligns outgoing data from the canonical schema to target system formats
Applies transformations for structure, format, and naming consistency
Reuses mappings across integrations and partners
Adapts mappings over time as schemas change
See how intelligent mapping accelerates onboarding and reduces manual effort as schemas evolve.
When a new system or partner is introduced, Data Nexus analyzes source and target schemas and uses an AI Mapping Agent to suggest field-level mappings based on structure, semantics, and historical patterns. Teams can review, adjust, and approve mappings—then reuse them across integrations.
As schemas change over time, the AI agent detects differences and recommends updates, reducing breakages and ongoing maintenance.
Faster partner onboarding with fewer manual mappings
Reduced impact from schema changes
Reusable, consistent mappings across integrations
Human-in-the-loop control with intelligent assistance
Designed to work with common industry formats and extensible domain-specific schemas.
Data Nexus supports normalization across a wide range of data models, from standard industry schemas to custom internal representations. Canonical schemas can be predefined, extended, or tailored to specific domains and partners.
JSON and REST-based APIs
Event and message-based payloads
Industry and partner-defined schemas
Domain-specific canonical models (e.g., customer, order, shipment)
Custom schemas defined per business needs
Configure, validate, and manage schema mappings without deep technical overhead.
Data Nexus provides a guided experience for reviewing schemas, configuring mappings, and approving AI-generated recommendations. Teams maintain full visibility and control while minimizing manual effort.
Schema visualization for source, canonical, and target models
AI-assisted mapping suggestions with review and approval
Versioned mappings with change tracking
Reusable configurations across partners and integrations
Practical scenarios where schema normalization and intelligent mapping deliver immediate value.
Rapid partner onboarding without custom point-to-point mappings
Normalizing data across multiple internal systems and external vendors
Reducing integration breakage caused by schema changes
Reusing standardized schemas across many integrations
Improving data consistency for downstream applications and analytics
Core capabilities delivered in the initial release to standardize and scale integrations.
Schema ingestion from source and target systems
Canonical schema definition and management
Bidirectional schema mapping (incoming and outgoing)
AI-assisted mapping recommendations with human approval
Reusable mappings across integrations and partners
Schema versioning and controlled updates
Additional MVP capabilities such as the unified API and compliance controls are described in their respective service sections.
Planned enhancements that expand intelligence, automation, and scale beyond the MVP.
Automated detection of schema changes with proactive impact analysis
Advanced AI-driven mapping optimization and confidence scoring
Expanded library of prebuilt domain and industry schemas
Self-service partner onboarding with guided mapping workflows
Deeper integration with governance, policy, and compliance controls
