DigiEdze AI · Knowledge Engineering
Give Your AI a Memory of the Business
We build the ontologies, semantic layers, knowledge graphs and memory stores that let AI reason about your entities, relationships and rules rather than pattern-match over text.
- knowledge graphs
- enterprise ontologies
- semantic layers
- vector stores
- metadata
- institutional memory
Reference architecture
- 06Enterprise ontology
- 05Entity resolution
- 04Knowledge graph
- 03Semantic layer over physical data
- 02Vector & hybrid memory
- 01Agent retrieval interface
What Changes When This Goes Wrong?
The same customer, asset or product is named four different ways across four systems.
Questions that need relationships — exposure, dependency, ownership — cannot be answered by passage retrieval.
Institutional knowledge lives with individuals and leaves when they do.
Knowledge Engineering Capability Grid
Enterprise ontology
A shared definition of your core entities, relationships and business rules, agreed across functions rather than imposed by one.
Knowledge graphs
Entity resolution and relationship modelling that support multi-hop questions retrieval alone cannot answer.
Semantic layer
Business meaning mapped onto physical data, so a natural-language question reaches the right table with the right definition of the metric.
Vector and hybrid memory
Embedding stores designed alongside the graph, so agents can traverse structure and search text in one retrieval.
Institutional memory
Decisions, runbooks, incidents and their outcomes captured as durable, queryable context.
Metadata and stewardship
Ownership, definitions and lifecycle for knowledge assets, so the layer stays true as the business changes.
How it works
A semantic layer that gives enterprise data meaning and connects it. Knowledge engineering defines how business concepts map to underlying data.
Technology
- Graph databases
- Entity resolution frameworks
- timbr.ai semantic layer
- Apache Atlas
- Ontology tooling
- Embedding stores
Use Cases
Implementation Across Verticals
Insurance
Policies, endorsements, claims and parties resolved into one graph for exposure questions.
Manufacturing
Assets, bills of material, suppliers and incidents linked for genuine root-cause reasoning.
BFSI
Counterparty and product hierarchies resolved so risk questions return one consistent answer.
Outcomes
What We Measure
Every engagement promises the organization’s data connectivity and how much the semantic model gets reused rather than rebuilt per use case.
- Entity resolution accuracy
- Multi-hop question coverage
- Definition consistency across functions
- Reuse across use cases
Questions
Knowledge Engineering, Answered Directly
Do we need a knowledge graph, or is RAG enough?
Retrieval handles 'what does this document say'. A graph handles 'what is connected to what, and what follows from that'. Questions about exposure, dependency, ownership and impact need structure. Most mature deployments use both together.
How do you keep an ontology from becoming a two-year project?
Model only the entities your live use cases touch, ship, then extend. An ontology built ahead of demand is a modelling exercise; one built behind real questions compounds in value.
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Build the Enterprise Memory
Tackle the AI challenges and build your enterprise knowledge by understanding what’s driving it. Talk with our expert and expect your enterprise to thrive.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT