GPU graph analytics
Centrality, community detection, and shortest or temporal paths at interactive latency, powered by RAPIDS and cuGraph.
Graphos · The graph engine
The GPU graph brain underneath your company's memory.
Graphos is Entilla's knowledge-graph engine. It turns raw organizational exhaust into a living, temporal graph that doesn't just store relationships but learns them.
The problem
Big graphs are useless if they're slow or wrong.
A knowledge graph with hundreds of millions of edges is worthless if a traversal takes minutes, and untrustworthy if entity resolution is a pile of hand-written rules. CPU graph engines fall over at that scale, and naive extraction leaves you with a fragmented, duplicate-ridden graph. So teams give up on the relationship questions that matter most.
What Graphos does
A trained graph, not a stored one.
Graphos ingests the entities and relationships pulled from your conversations, documents, and code, then runs graph neural networks and GPU graph analytics over them. It merges "Bob from billing" across a dozen systems, surfaces dependencies nobody documented, and answers point-in-time questions like "what did we know about Acme in Q3?" Every other Entilla product reads from it.
Core features
What's inside.
GNN entity resolution
Learned merging and link prediction instead of brittle rules.
Temporal queries
Point-in-time answers: what the org knew, and when.
Incremental updates
Change data capture from source systems keeps the graph fresh.
Permission-aware subgraphs
ACLs enforced at the edge level, so retrieval never leaks.
Quality scoring
Drift detection and graph-quality scores catch decay early.
Why it needs GPUs
Accelerated compute is the core workload.
RAPIDS / cuGraph
Billion-edge analytics (centrality, community, multi-hop temporal paths) at interactive latency. This is the literal core compute primitive.
CUDA / cuDNN
Custom kernels for temporal traversal and GNN message passing that CPU engines can't match at scale.
DGX / H100
GNN training over billion-edge graphs for entity resolution, link prediction, and node classification.
TensorRT-optimized GNN inference is served through Triton, and regulated on-prem deployments run on NVIDIA AI Enterprise.
Defensibility
The moat compounds monthly.
Per-customer GNNs and embeddings improve every month from labeled outcomes. That's a data flywheel latecomers can't shortcut, and migrating a billion-edge graph off Entilla is enormously costly once it's embedded.
Where it sits
The substrate everything reads from.
Mnemo retrieves over Graphos, Verix verifies against it, Nexora acts on it, Synthos simulates it, and Holoscope feeds it from the edge. It stays entirely inside Entilla's AI Knowledge Systems category.