Data and meaning
Normalization, entity resolution and relationship analysis make data reusable across questions. Correlations are evaluated with source, time and context; they do not establish causation on their own.
System architecture
Enterprise system architecture.
Across large and complex data structures, we preserve provenance, connect shared records and translate user direction into governed workflows.
Xong / Connected operating model
Your data. Your control.SourcesUnified dataHuman + AIOperations
Permissions · provenance · execution history
See your data in a shared context.
Enterprise applications, documents, conversations and field data. We connect sources of different volumes and structures while preserving provenance and access boundaries.
- ERP / CRM / BPM
- Documents & media
- Sensors & events
Turn complexity into manageable knowledge.
We structure large, complex datasets from multiple sources, normalize fields, reconcile shared records and analyze correlations and relationships across sources.
- Normalization
- Entity resolution
- Relationship analysis
From intent to analysis. From analysis to action.
Dynamic AI agents assemble relevant data and tools around the user’s objective. Permissions, scope and approval rules determine which operations can proceed automatically.
- User direction
- Models & agents
- Review & approval
The whole operation, in one working environment.
We connect analysis, tasks, documents and decisions to enterprise process software. Teams access data, see work in progress and manage automated or semi-automated workflows from the same place.
- Shared records
- Process management
- Traceable outcomes
Dynamic agents
User objectives become a working scope. Agents assemble steps from permitted data and tools; results are presented with provenance and execution history.
Enterprise process layer
Tasks, roles, decisions and system operations revolve around shared business records. Existing ERP, CRM and BPM connections are configured for the organization.
How components work together
Architecture that preserves sources and keeps work moving.
Storing a source, interpreting it with a model and executing an external action are separate responsibilities. Connecting them does not turn them into one opaque AI box.
Data foundation
Raw sources, versions, standardized records and relationships.
Processing and retrieval
Data-appropriate processing and source-linked results.
Work execution
Steps, tools, permissions, error paths and target-system responses.
Review and improvement
Human judgment, correction history and comparable evaluation.
Components and operating approach
Work context that survives across layers.
Select a topic to explore the relationships between information, responsibility and delivery.
01
Data contract
Establish source files, identities, versions and access scope before processing. Normalized values link back to original sources.
- 01Data contract
- 02Execution contract
- 03Decision contract
- Data contract
- Data contract
- Execution contract
- Execution contract
- Decision contract
- Decision contract
02
Execution contract
Track agent objectives, permitted tools and continuation criteria in the same execution context. Failed steps are not presented as successful work.
- 01Data contract
- 02Execution contract
- 03Decision contract
- Data contract
- Data contract
- Execution contract
- Execution contract
- Decision contract
- Decision contract
03
Decision contract
Link proposals, human decisions and external records with distinct identities. This preserves decision evidence during retries and reviews.
- 01Data contract
- 02Execution contract
- 03Decision contract
- Data contract
- Data contract
- Execution contract
- Execution contract
- Decision contract
- Decision contract
An operating-model illustration, not live operations or measured performance results.
In practice
One operation, three responsibilities.
Connected system
Source systems
Shared dataWithin source and access scope
Model and agentWithin source and access scope
Review and executionWithin source and access scope
Processing and control structure
Data responsibility
Raw sources, standardized records and derived results stay distinct. Identity, versions and source relationships carry into new analysis while access boundaries remain intact.
Execution responsibility
Plans, tool calls, validation and retries define work state. Completing a step does not by itself mean an external action succeeded.
Decision responsibility
Uncertainty, high impact or authorization requirements route work to human review. Original suggestions, user corrections and final outcomes need separate traceable records.
Work with us
Discuss your project with us.
Let’s assess your operations, data landscape and priorities together.