Turn enterprise data into context AI can understand
Sync structured databases and unstructured files into one governed data plane, model their business meaning, and let agents analyze both with traceable sources.
Data Hub
Ontology · Lineage
Ask the BI Agent
How much did revenue grow in Q2 2026, QoQ?
+18.4%
$23.1M → $27.4M
From source systems to answers with evidence
Data Hub keeps raw systems intact, creates isolated and current analytical copies, and gives every agent the context to choose the right table, document and relationship.
Sync S3 into a knowledge base AI can actually read
Bind AWS S3, MinIO or SeaweedFS, select a folder, and keep its files synchronized. UnieAI Parser extracts text, OCR and structure before chunking and vector indexing.
- Initial scan plus incremental create, update and delete propagation
- PDF, Office, images and audio in one ingestion path
- Content deduplication reduces repeated processing
- Per-file progress, failures and source paths stay visible
Mirror SQL safely, then turn topology into business context
Sync operational databases by batch or CDC into a tenant-isolated warehouse. Data Hub preserves the raw schema and identifies tables, columns and foreign keys; Studio uses that topology to draft business objects, relationships and metrics for joint business and engineering review.
- PostgreSQL, MySQL, SQL Server and HTTP API sources
- Query the synchronized copy without loading production systems
- Near-real-time CDC where the source supports it
- Draft Objects, Links and candidate metrics from source topology
Ask a business question. Get SQL, analysis and a chart.
The agent reads the confirmed ontology before it queries. It can select the right tables, write SQL, combine results with supporting documents and render an inspectable BI answer.
- Natural language to SQL with ontology context
- Charts and analytical summaries from governed data
- Cross structured records and unstructured evidence
- Source table and document path remain visible
Ontology is not another data platform. It is Data Hub's semantic control layer.
Data Hub spans connection, synchronization, storage and lineage through ontology. Its built-in ontology sits between data and AI action, packaging Objects, Links, Policies, Actions and Evidence as governed, deployable runtime assets.
Mirror business truth
Preserve source schemas, documents and lineage without replacing systems of record.
Model the business
Draft Objects, Properties, Links and metrics from source topology.
Confirm governance
Business and engineering define permissions, Policies and evidence in Studio.
Package safe actions
Ship confirmed semantics and Actions as reusable agent runtime assets.
Decide and read back
Agents act within policy, retain evidence and read execution results back.
ANALYTICS
BI / Reports / Q&A
KPI
12.8M
Tokens / day
↑ 12.6%
WORKFLOWS
Agents / Automation / Decisions
Triggers
Agent Orchestration
Actions
INTEGRATIONS
APIs / Apps / MCP
CRM
ERP
Finance App
ONTOLOGY / SEMANTIC LAYER
Data Hub × Ontology × Named Metrics
UNIEAI DATA HUB
UNIEAI MODEL ZOO
Fresh, isolated and traceable by design
Tenant isolation
Warehouse data, permissions and query routing remain separated per tenant and connection.
Data lineage
Sync versions, source URIs, table names and full document paths stay attached to results.
Operational visibility
Track sync progress, processed rows, file status, failures, freshness and usage from one control plane.
Give your agents a data foundation they can trust
Bring one database or S3 folder. We will show the complete path from synchronization to a sourced AI answer.