Knowledge Graph & Data Qualification
Find, map, and qualify data — then interact through graph-based intelligence.
Knowledge graph and data qualification combines find-map-get data discovery with graph-based intelligence — so teams understand which fields matter, how they relate, and how agents should use them.
What this accelerator delivers.
Enterprises cannot agentify what they do not understand. Unknown field relationships, undocumented entity links, and unqualified data cause agents to make wrong decisions — and compliance teams to block deployment. This accelerator implements find-map-get discovery with graph-based intelligence: qualifying which fields matter, how entities relate, and how past decisions inform future ones. It connects systems of record with the memory agents need to reason reliably — Layer 02 of the 4-Layer Intelligence Stack. Embedded engineers build the graph in your environment, not as a standalone analytics project, but as production infrastructure that agents query during live workflows. The outcome is qualified, relationship-aware data that agents and engineers can trust.
Find, map, and qualify enterprise data at scale
Graph-based intelligence for agent reasoning
Links systems of record to decision context
Memory layer for the 4-Layer Intelligence Stack
Concrete artefacts, not slide decks.
Find-map-get assessment
Systematic discovery of fields, entities, and relationships across the data estate.
Knowledge graph implementation
Production graph schema, ingestion pipelines, and query interfaces in your environment.
Data qualification framework
Rules and scoring for field relevance, freshness, and agent suitability.
Agent query integration
Graph query patterns wired into agent workflows for production reasoning.
Four phases to production go-live.
Embed & discover
FDEs embed inside your business, learn the domain, and scope the highest-value use case for this accelerator.
Unify context
Run find-map-get discovery and implement knowledge graph schema with ingestion from priority source systems.
Configure & evaluate
Configure qualification rules, agent query patterns, and validate graph accuracy against test scenarios.
Deploy & monitor
Go live securely in your cloud with FDEE-led monitoring, continuous evaluation, and proactive tuning.
Where this accelerator applies.
Customer entity resolution
Graph-based linking of customer records across core banking, CRM, and KYC systems.
Claims relationship mapping
Entity graphs connecting policies, claims, parties, and documents for FNOL agents.
Decision memory layer
Graph store capturing past decisions to inform future agent reasoning.
Production outcomes, not pilot metrics.
Field qualification coverage achieved before agent configuration begins.
Faster entity resolution for agent queries vs. manual lookup.
Production graph operational for priority agent workloads.
Related accelerators
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Frequently asked questions
- Why use a knowledge graph for enterprise AI?
- Knowledge graphs expose relationships between fields, entities, and decisions — helping agents and engineers qualify data before it reaches production workflows.
- Do we need a graph database already?
- No. The accelerator implements graph infrastructure in your preferred cloud — selecting the right store for your scale and query patterns.
- How does the graph connect to agents?
- Agents query the graph during workflows via MCP connections — retrieving entity relationships and decision context in real time.
- What is data qualification?
- Systematic scoring of field relevance, freshness, and suitability for agent workloads — preventing unqualified data from reaching production agents.