Knowledge graphs that answer questions tables can't
Causal Labs designs and engineers knowledge graphs — Neo4j, Memgraph, NebulaGraph and Cypher — that model entities, relationships and time so businesses can query connections a relational table structure hides. Delivery has included temporal knowledge graphs with constraint validation and Cypher-based analytics.
Causal Labs designs and engineers knowledge graphs — Neo4j, Memgraph, NebulaGraph and Cypher — that model entities, relationships and time so businesses can query connections a relational table structure hides. Delivery has included temporal knowledge graphs with constraint validation and Cypher-based analytics.
Why relational tables hide the questions that matter most
'Which of our suppliers share a common upstream vendor we didn't know about' or 'how are these two flagged accounts connected three hops apart' are graph questions. In a relational schema they mean a chain of joins that gets slower and harder to write with every additional hop. A graph database makes relationship-traversal the native operation instead of the expensive one.
How we approach a graph database engagement
Requirements gathering
We identify the actual relationship-traversal questions the business needs answered, which shapes the graph model.
Design
Entity, relationship and temporal-edge modeling, with constraint validation to keep the graph consistent as it grows.
Build
Data loading pipelines and Cypher-based analytics/query layers for the traversal patterns identified upfront.
What's included
Entity & relationship modeling
A graph schema designed around the actual traversal questions, not a generic entity-relationship diagram.
Temporal edges
Relationships modeled with time, so 'who was connected to what, when' is queryable, not just the current state.
Constraint validation
Data integrity rules that keep the graph consistent as new data is loaded.
Cypher analytics
Query and analytics layer built for the traversal patterns your team actually needs.
- Graph schema and data model
- Data loading pipeline
- Cypher query/analytics layer
- Constraint validation rules
The default graph database choice for most engagements, given its maturity and Cypher tooling.
Used where in-memory, low-latency graph queries are the priority.
Used for very large-scale distributed graph workloads.
Designed and loaded knowledge graphs with temporal edges, constraint validation and Cypher analytics.
typical delivery window
Questions about this service
How do we know if our problem actually needs a graph database?
If your key questions are about relationships and traversal depth — 'connected within N hops', 'shared attributes across a network' — a graph database usually wins. If your questions are mostly aggregation over flat records, a relational database is often still the better fit, and we'll tell you that honestly.
Have data where the connections matter more than the individual records?
Tell us what questions you're trying to answer about how your data is connected.
Start a conversation