What every term means
Semantic foundation for enterprise AI
Before AI-ready, become meaning-ready.
Storage, pipelines and models are not enough. AI needs governed definitions, relationships, rules, permissions, provenance and business context. We build that semantic foundation into your data platform.
Why AI struggles
Your model sees tokens.
Your business runs on meaning.
Without a formal context layer, AI must guess how enterprise terms, systems and decisions relate.
Without ontology
Probabilistic context
- Different definitions across sources
- Ambiguous identity and relationships
- Weak lineage and access context
- Hidden business rules
- Answers that are difficult to verify
→Semantic
transformation
transformation
With ontology
Governed understanding
- Shared enterprise vocabulary
- Resolved entities and graph context
- Explicit provenance and policy
- Machine-readable rules and constraints
- Evidence-backed explanations
Ontology as the brain
Six kinds of context AI needs.
The ontology supplies durable enterprise knowledge; the graph supplies current facts; vector retrieval supplies relevant content; rules govern what AI can infer and do.
SDSAI Context LayerOntology + graph + policy
Which records are the same thing
How facts connect
What must be true
Where knowledge came from
Who may see and act
AI-ready platform blueprint
Semantics embedded
at every layer.
05Experiences
CopilotsAI agentsDecision intelligenceMarket dashboardsRegulatory reporting
04AI & Analytics
GraphRAGFeature engineeringML modelsReasoningExplainability
03Semantic Context
Enterprise ontologyKnowledge graphBusiness rulesPolicyProvenance
02Data Products
MarketPartyInstrumentPortfolioRisk
01Data Foundation
StreamingLakehouseWarehouseDocumentsAPIs
What changes for an AI agent
From fluent response to defensible decision.
An ontology-aware agent does more than retrieve a similar document. It resolves the user’s intent to enterprise concepts, traverses authorized relationships, evaluates rules, gathers evidence and returns a traceable answer.
Design your AI context layer ↗01Interpret intent
Map language to governed concepts.
02Resolve context
Identify entities, roles, time and scope.
03Traverse knowledge
Follow relevant, authorized relationships.
04Apply policy
Validate constraints and business rules.
05Explain output
Return evidence, provenance and reasoning.
Activation use cases
One semantic foundation.
Many intelligent experiences.
Financial research copilot
Combine filings, news, market data and portfolio context with evidence-linked answers.
Market direction intelligence
Connect events, sentiment, factors, prices and risk regimes into explainable signals.
Data product discovery
Find, understand and safely use data through meaning, ownership, quality and lineage.
Risk & compliance agent
Evaluate obligations, controls, exposures, exceptions and evidence across connected systems.
Operational intelligence
Understand dependencies, events, incidents and impacts across the financial value chain.
Enterprise knowledge search
Ground retrieval in organizational concepts, access policies and authoritative sources.
Make AI enterprise-aware
Build the context layer before scaling the model.
We help transform fragmented enterprise data into governed knowledge your AI can understand and explain.