Ontology and entity design
Domain models co-designed with your experts — the entities and relationships that actually matter to the business.
AI Infrastructure
Enterprise Knowledge Graphs
Make relationships explicit — customers, products, contracts, controls — so agents and copilots can reason across them, not just search near them.
- 10×faster answers to multi-hop questions
- 95%+entity resolution accuracy achievable
- 1connected view across your systems
Capabilities
Relationships, made computable
Entity and relationship intelligence that gives AI systems multi-hop reasoning over your business.
Entity resolution
The same customer, product or counterparty recognized across systems, spellings and decades of data drift.
Graph-augmented retrieval
RAG that traverses relationships, not just similarity — answering questions vectors alone can't.
Governance and lineage
Every node and edge sourced, versioned and access-controlled.
How it works
Answers that require connecting the dots
"Which customers are exposed to this supplier's recall?" is not a search problem — it's a graph traversal. We build the graph and wire it into your AI.
Graph construction
From scattered records to a connected model
We extract entities and relationships from your databases, documents and event streams, resolve identities across sources, and maintain the graph continuously as systems change.
- Automated extraction from structured and unstructured sources
- ML-assisted entity resolution with human adjudication queues
- Continuous sync with change detection and quality gates
Ingest→
Chunk→
Embed→
Index→
Retrieve
PDFConfluenceSharePointSQLTicketsEmail
Grounded answer…with citations [1] [2] and permissions applied
Reasoning
Multi-hop questions, single answers
Graph-augmented retrieval combines traversal with semantic search: agents follow ownership chains, dependency trees and exposure paths, then ground their answers in the documents behind each edge.
- Traversal + vector retrieval fused per query
- Path explanations showing how the answer connects
- Citations to the source records behind every edge
Shared stateHandoffsRetriesTraces
Operations
A graph you can govern and trust
Access controls at node and edge level, full lineage from every fact to its source system, and quality dashboards that show coverage and freshness — infrastructure, not a science project.
- Node/edge-level access control mirroring source ACLs
- Lineage from graph facts to originating records
- Coverage, freshness and resolution-quality monitoring
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Use cases
Where knowledge graphs earn their keep
Customer 360 reasoning
Households, hierarchies and relationships powering service and risk AI.
Supply chain exposure
Multi-tier supplier risk traced from event to affected product.
Compliance mapping
Regulations linked to policies, controls and evidence.
Product compatibility
Parts, configurations and dependencies made queryable.
Fraud networks
Hidden connections across accounts, parties and transactions.
Institutional memory
People, projects and decisions connected for discovery.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Our RAG could find documents; it couldn't tell us which funds were exposed through three layers of holdings. The graph answered that in seconds — with the path shown.
Works with your stack
Built on proven graph foundations
Graph databases, pipelines and your existing data platforms — integrated, not replaced.
- Neo4j
- Amazon Neptune
- TigerGraph
- Postgres
- Snowflake
- Databricks
- Kafka
- Anthropic Claude
- OpenAI
- Elasticsearch
- dbt
- Kubernetes
FAQ
Common questions
Do we need a knowledge graph or just better RAG?
If your questions are lookups, tuned RAG suffices. If they require connecting entities across systems — exposure, ownership, dependency, impact — you need the graph. We'll tell you honestly which one your use cases demand.
How much effort is entity resolution really?
It's the hard part, and we treat it that way: ML candidate matching, confidence thresholds, human adjudication for ambiguous cases, and precision tracking over time. Plan for iteration, not magic.
Does the graph go stale?
Not if it's built as infrastructure: continuous sync from source systems, change detection and freshness monitoring keep it current — staleness alerts fire before answers degrade.
Can it respect our access controls?
Yes — source-system permissions are mirrored to node and edge level, so a user's question can only traverse what that user could see directly.
Next step
Make your AI understand how things connect
Bring one multi-hop question your systems can't answer today — we'll show you the graph that answers it.