1
Document
Ingest and normalize data from any source.
Public-sector data governance
The Intelligence Behind Every Data Decision
Harmonize metadata, indicators, and data definitions across ministries, agencies, and local governments.
Document
Graph
Agent
Decision
Live explanatory model
Current state
Document intake
Source material enters a governed context before any comparison, mapping, or reasoning begins.
Trusted decision flow
From data to trusted decision
A continuous flow that transforms institutional information into governed action.
2
Graph
Resolve entities and relationships in a governed knowledge graph.
3
Agent
AI agents analyze, correlate, and surface insights.
4
Decision
Humans review, decide, and take action.
Ingest
Source files become reviewable input before reasoning begins.
Indicator Mapping
Reference codes and sector tags are linked into one graph context.
Conflict Review
Agents compare formats, columns, and metadata quality before escalation.
Quality Signals
Coverage, source type, and confidence scores stay visible for review.
Decision ready
Human reviewers see the trace before approving any conclusion.
Why this matters
Harmonization fails when institutions share data without shared meaning.
Fragmented sources
Definitions, indicators, and reporting rules remain split across documents and systems.
Conflicting interpretations
Teams compare data that looks aligned on paper but diverges in logic, units, or method.
Weak decision traceability
Human review often arrives late, after institutions have already lost context and confidence.
System model
A technical system built to make governance legible
Knowledge graph as controlled memory
The graph keeps definitions, indicators, and institutional relationships connected in one reviewable structure.
Agent reasoning as structured comparison
Agents activate only to compare, surface conflict, and propose alignment paths, never to replace oversight.
Human authority as final resolution
When the decision point arrives, the system narrows attention to one accountable review moment.
Interface preview
See the graph, metadata, and review context in one workspace.
Workflow
One Pipeline From Intake to Decision
01
Auto Data Transformation
Normalize raw institutional data into harmonized structures with less manual prep.
02
Source connectors
Link spreadsheets, APIs, and operational systems into one governed ingestion layer.
03
Knowledge Graph
Map datasets, indicators, and definitions into one connected review structure.
04
Multi Agent review
Run parallel checks to surface conflicts, alternatives, and evidence for human review.
Outcomes
Built to reduce ambiguity before institutions act.
Faster technical alignment
Teams compare meaning earlier, before conflicting definitions harden into reporting friction.
Clearer stakeholder understanding
Mixed audiences can see how governance, reasoning, and review fit together in one system.
Reviewable final decisions
Recommendations stay connected to evidence, relations, and the human authority that approves them.
Next step
See how Metagraph fits an existing data governance workflow.
For technical teams and institutional stakeholders evaluating harmonization, governance, and accountable review.
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