Solve the core problems of enterprise data use
Fragmented data
Data is spread across systems and files with no unified entry point or visibility.
Hard to trust
Definitions, origin, ownership and permissions are unclear to business users.
AI gets the data wrong
Without governance, business meaning and knowledge links, general-purpose AI cannot reliably understand enterprise differences or choose the right data.
Core Capabilities
Composable capabilities adapt to different organizations, industries and deployment environments.
Multisource ingestion
Connect databases, business systems and unstructured content through connectors, uploads and controlled APIs.
- Connectors
- Incremental sync
Unified data catalog
Present data assets, business meaning, ownership, quality and usage status in one catalog.
- Catalog
- Metadata
Knowledge preparation
Parse, segment, index and vectorize documents and other multimodal content for AI use.
- Parsing
- Vectorization
Secure data services
Expose governed data through search, APIs and agent tools with permission filtering and auditability.
- APIs
- Agent tools
Platform Architecture
- Data SourcesSystems, databases and files
- IngestionConnect, synchronize and parse
- GovernanceCatalog, quality, access and lineage
- KnowledgeIndexes, vectors and knowledge services
- ApplicationsSearch, Q&A, analytics and agents
Integration Methods
- Connect data through supported connectors, files, object storage or controlled APIs after a technical assessment.
- Integrate with enterprise identity, access and logging systems to preserve existing security boundaries.
Security & Deployment
- Evaluate private, dedicated or controlled cloud deployment according to data sensitivity and network boundaries.
- Confirm access filtering, audit logs, secrets and storage policies before the proof of concept.
Verifiable Evidence & Limits
These are checkable acceptance signals and explicit boundaries—not unverified performance promises.
Acceptance Evidence
- Validate the implementation with traceable sources, permission tests and documented acceptance criteria.
- Measure coverage, update freshness and task completion against an agreed test set.
Capability Limits
- Results depend on source quality, metadata completeness, permissions and update processes.
- High-impact decisions and actions require explicit controls and human review.
Frequently Asked Questions
- How should an organization start with Multimodal Data Platform?
- Start with one high-value workflow, a defined user group, known data sources and measurable acceptance criteria, then expand after testing quality and security.