Enterprise knowledge,traced from evidence to decision.
Cognivax connects knowledge, evidence, reasoning cases, decisions and governance in one structured, auditable environment.
Application Target, click to expand
Reasoning across interconnected, multi-source knowledge
Cognivax is an AI-native Enterprise Reasoning & Knowledge Engine designed to solve the most persistent failure mode of enterprise AI: the inability to reason across complex, interconnected, multi-source knowledge.
Cognivax addresses this challenge by combining large language models with dynamically constructed knowledge graphs to create a reasoning engine that understands not just what information exists, but how entities, concepts, and events relate to one another across the enterprise.
The platform's core differentiator is its ability to perform graph-enhanced reasoning: when a user asks a question, Cognivax traverses the knowledge graph to identify relevant entities and relationships, decomposes the question into reasoning sub-tasks, and synthesizes evidence from multiple graph paths before generating a grounded, citation-backed answer.
Compute Requirement
The platform architecture requires massive accelerated computing capabilities to construct and maintain knowledge graphs at enterprise scale, execute real-time graph traversal, and run Nemotron reasoning models.
Watch a question become evidence
Pick a question below. Cognivax resolves entities, traverses the graph, decomposes the problem, and synthesises a citation-backed answer, every step recorded.
“Which suppliers are exposed to the semiconductor shortage impacting Q3 deliveries?”
Reasoning Trace
0/5 steps- 1Entity Resolution6ms
- 2Graph Traversal14ms
- 3Community Detection9ms
- 4Problem Decomposition210ms
- 5Grounded Synthesis340ms
Grounded Answer
synthesising from graph evidence…
Technical stack
Cognivax follows a modern AI-native architecture designed for knowledge graph construction, graph-enhanced retrieval, multi-hop reasoning, and grounded generation workloads.
Languages & Runtime
- Python
- PyTorch
- FastAPI & gRPC
- React
Service and model runtime surface.
Data & Graph Storage
- Neo4j / ArangoDB
- PostgreSQL & pgvector
- Vector DB (Elasticsearch, Milvus)
- Redis
Graph, vector, and relational persistence.
Pipelines & Analytics
- Apache Spark
- Apache Kafka
- MLflow
- OpenTelemetry
Streaming ingest and experiment tracking.
Platform & Ops
- Docker
- Kubernetes (Amazon EKS)
- Prometheus & Grafana
- Hybrid search & metadata filtering
Deployment, lifecycle, and retrieval control.
Current NVIDIA Integration Status
Cognivax is currently architecting its AI infrastructure for NVIDIA accelerated computing. NVIDIA technologies are critical to the production roadmap, particularly for enabling GPU-accelerated knowledge graph construction, real-time graph traversal, and Nemotron reasoning model inference.
Planned architecture
Cognivax requires a comprehensive NVIDIA technology stack focused on knowledge graph construction, graph-enhanced reasoning, multi-hop retrieval, and grounded generation.
Purpose, GPU-accelerated graph analytics for knowledge graph construction, traversal, and multi-hop reasoning.
Planned Implementation
- Scalable Graph Analytics: GPU-accelerated graph analytics across billions of nodes and edges, integrating with NetworkX and RAPIDS cuDF to power multi-hop searches and algorithms such as PageRank and community detection.
- GraphRAG Querying: GPU-powered knowledge graph querying and retrieval, enabling the reasoning engine to traverse entity relationships across the enterprise knowledge graph in milliseconds.
- Community Detection: Leveraging cuGraph's Leiden community detection algorithm, central to Microsoft's GraphRAG implementation, to identify thematic clusters and critical relationship hubs within the knowledge graph.
- NetworkX Backend Acceleration: Using nx-cugraph as a backend for NetworkX, enabling existing Python graph analytics code to run on GPUs with near-zero code changes while achieving order-of-magnitude performance improvements.
Expected Benefits
Justification on AWS infrastructure
Cognivax is designed to construct and maintain enterprise-scale knowledge graphs, execute real-time multi-hop reasoning, and run Nemotron reasoning models.
Specification, Amazon EC2 P4d
NVIDIA A100 Tensor Core GPUsPlatform Workloads
- LLM-driven knowledge graph construction from enterprise document corpora
- GPU-accelerated graph analytics and multi-hop traversal across billions of nodes and edges
- Nemotron reasoning model inference for dynamic problem decomposition and iterative refinement
- Real-time embedding generation and reranking for retrieval precision
- Grounded generation with citation-backed answers
- Continuous knowledge graph enrichment and maintenance
- Multi-model inference with dynamic batching and ensemble pipelines
Technical Justification
NVIDIA H100 and A100 Tensor Core GPUs provide the computational performance required for processing massive document corpora, building high-dimensional vector indexes, running complex graph analytics, and executing large reasoning models.
Access to AWS GPU credits through the NVIDIA Inception Program will allow Cognivax to validate its reasoning engine on production-relevant hardware.
Performance Benchmarks, click any metric
Q3 to Q4 execution plan
Ten milestones spanning knowledge graph construction, graph-enhanced reasoning, model customization, and enterprise-grade deployment. Click any milestone to mark it complete.
Reviewer credentials
Access details for evaluation environments. Credential values are redacted in this profile and are supplied through NVIDIA's secure Inception submission channel.
Environment Logins
Email and password fields are intentionally withheld from this document. Values will be provided directly to the NVIDIA Inception review team.
Prepared for the NVIDIA Inception Program
This technical application profile has been prepared for submission to Nivax X.1 / the NVIDIA Inception Program.