What breaks first.
Service Catalog
Five capability layers, eighteen services.
Organized the way video problems actually surface. Find and follow what’s on
camera, understand what’s happening, make it searchable in plain language, catch
what breaks the pattern, then turn it into operational metrics.
1.1 Discovery & Assessment
Before you build the graph, know what you're working with.
Inventory your data sources, assess their graph-readiness and identify quality gaps before anything gets modeled. Establish what's available, what's usable and where the real effort will be.
Deliverables:
- Source inventory
- Readiness scores
- Feasibility report
Result:
- A clear starting point
- Without building blind
1.2 Data Engineering
Get the data ready before you ask it to connect.
Ingest structured, semi-structured and unstructured data across enterprise sources. Clean, parse and normalize information so it can move into the graph as a reliable, usable foundation.
Deliverables:
- Clean structured data
- Ingestion pipelines
- Quality logs
Result:
- Reliable inputs
- Ready for connected intelligence
1.3 Semantic Engineering
Give your data a shared language.
Extract entities and relationships across sources, then define the taxonomy and business vocabulary that connects them. Turn inconsistent terminology into concepts the business and the graph can both understand.
Deliverables:
- Taxonomy
- Business glossary
- Entity/relationship maps
Result:
- Consistent meaning across disconnected data
2.1 Ontology & Knowledge Engineering
Model the knowledge before you ask it to reason.
Design domain and core ontologies around the concepts, relationships and rules that matter to your business. Build governance into the model so the graph remains understandable as it grows.
Deliverables:
- Ontology model
- Class hierarchies
- Governance rules
Result:
- A knowledge model the business can actually trust
2.2 Graph Modeling
Choose the graph around the questions you need to answer.
Design the graph model around your data, relationships and query patterns rather than forcing everything into a vendor default. Select the structure that fits how your organization needs to reason.
Deliverables: Schema design, node/relationship model, SHACL validation.
- Schema design
- Node/relationship model
- SHACL validation
Result:
- A graph designed for the questions that matter
2.3 Graph Transformation & Migration
Move the graph without losing what it knows.
Transform and migrate graph data across RDF, RDF★ and LPG models while preserving relationships, mappings and traceability. Keep the reasoning behind every transformation visible throughout the move.
Deliverables:
- Migrated graph
- Mapping rules
- Audit trail
Result:
- A cleaner transition, without losing the knowledge underneath
3.1 Integration
A graph is only useful if it can talk to the systems around it.
Connect the knowledge graph with systems of record through ETL/ELT pipelines, APIs, federation and streaming synchronization. Keep information moving as the underlying enterprise data changes.
Deliverables:
- Live pipelines
- Connectors
- Sync logs
Result:
- Connected knowledge that stays current
3.2 Knowledge Processing & Enrichment
Connect the same entity wherever it appears.
Resolve and link entities across systems, then enrich the graph with inference, correlation and provenance. Turn repeated, fragmented references into a connected view of the same underlying knowledge.
Deliverables:
- Resolved entities
- Enriched graph
- Provenance metadata
Result:
- Fewer duplicates
- Deeper context
- Stronger connections
Graph Analytics & Intelligence
Find the patterns that aren't visible in the rows.
Use graph traversal, community detection, similarity and recommendation algorithms to uncover relationships and structures hidden across your data. Move from connected information to patterns worth investigating.
Deliverables:
- Pattern reports
- Dashboards
- Recommendations
Result:
- Deeper relationships
- Clearer signals
- Better decisions
4.1 AI Enablement & Analyzer
Ask the graph the questions your data never could.
Ground LLMs and enterprise copilots in connected knowledge using vectorization, semantic search and GraphRAG. Give AI the relationships and context it needs to answer complex questions without losing the trail back to the source.
Deliverables:
- Grounded answers
- Embeddings
- Knowledge assistant
Result:
- Better answers
- Deeper reasoning
- Less guesswork
4.2 Governance, Quality & Trust
Know where the answer came from—and who was allowed to see it.
Track lineage, ownership and access across the knowledge graph with validation, versioning and compliance controls. Make the graph auditable and keep its outputs accountable as it evolves.
Deliverables:
- Lineage records
- Access controls
- Audit reports
Result:
- Traceable knowledge
- Controlled access
- Audit-ready intelligence
4.3 Deployment & Managed Services
Getting the graph built is only the beginning.
Take the knowledge graph into production across cloud, on-premise or hybrid environments. Monitor performance, scale with demand and keep the platform supported as enterprise requirements change.
Deliverables:
- Production deployment
- Monitoring
- SLA-backed support
Result:
- FA graph that keeps working as the enterprise grows