Layer 03
APEX Copilot
Agentic AI grounded in the Product Graph, not the open web. Multi-hop reasoning across 88 node types, with citations to the underlying node and signal source URL.
The Platform
One graph. Three layers. Built on a live data engine.
Product Graph is a proprietary data engine + collaborative workspace + AI copilot — purpose-built for the teams who win on intelligence.
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Three Layers
One platform that does the work of four.
A continuously refreshed knowledge graph, persona-tuned workspaces, and an agentic copilot — all built around a single source of truth.
Layer 02
Collaborative Workspace
Persona-tuned modules for Product Ops, Competitive Intelligence, Sales Enablement, and RFX — sharing one source of truth so what one team learns, every team can act on.
Layer 01
Data Engine
Continuously enriched knowledge graph of 250,000+ enterprise software products across 2,606 categories, with 1,000+ live signal sources and 2.5M+ software reviews.
Built on Graph RAG, not Vector RAG. Built on real product data, not scraped text. Built for enterprise software teams, not generalists.
Four Jobs, One Platform
One workspace for the four teams that win on intelligence.
Product Operations, Competitive Intelligence, Sales Enablement & RFX, and AI Context Management — your buyer is the same person across all four. So is the data.
Stop paying four bills for one job.
The same buyer signs the contracts for CI, RFP, sales enablement, and AI grounding. Their teams need the same intelligence. PYRAMYD ships one platform, one copilot, one source of truth — backed by the only pre-populated enterprise-software knowledge base in the market.
The Architectural Advantage
Graph RAG outperforms vector retrieval — by the numbers.
Why a typed, traversable knowledge graph delivers more reliable multi-hop reasoning than disconnected document chunks.
Vector RAG
Disconnected document chunks. Brittle on multi-hop reasoning.
Accuracy range: ~20% zero-shot to ~80% fine-tuned
Graph RAG (PYRAMYD)
Typed entities + relationships. Multi-hop reasoning with citations.
Accuracy range: 60-95% (decentralized graphs reach 90-99%)
VS
Benchmark Comparison
| Aggregation queries | Vector RAG 8% relevance | Graph RAG 23% | 3× |
| Cross-document reasoning | Vector RAG 8% | Graph RAG 33% | 4× |
| Avg accuracy lift | — | Knowledge graph grounding | +35-54% |
PYRAMYD's stack: Mastra Graph RAG + CopilotKit UI + 78-node graph schema + 200+ connectors + 1,000+ live signal sources.
Source: Gartner 2026 Hype Cycle for Agentic AI · Fluree · Neo4j · Atlan · AIMultiple
Built Enterprise-Grade from Day One
A six-layer stack that's audit-ready by design.
Postgres + Drizzle ORM, AWS-native (RDS, RDS Proxy, ECS), Mastra Graph RAG, CopilotKit agent UI, row-level security, tenant-isolated graph slices, inline vector embeddings with HNSW indexes.
Architecture
APEX Copilot
Mastra Graph RAG + CopilotKit agent UI
Customer Workspaces (UI)
Persona-tuned modules for Product Ops, CI, Sales Enablement, RFX
API & Auth Layer
3-tier auth, row-level security, tenant-isolated graph slices
Knowledge Graph
Postgres + Drizzle ORM, 88 node types, inline embeddings + HNSW
Signal Ingestion Pipeline
ETL across 1,000+ continuously refreshed sources
Connector Layer
200+ pre-transformed connectors · 183 bi-directional
Compliance
- SOC 2 Type 2In progress
- ISO 27001In progress
- ISO 42001In progress
- GDPRCompliant
- CCPACompliant
- AWS Well-ArchitectedReviewed
- EU AI Act Article 50Aug 2026 ready
EU AI Act Article 50
Transparency mandate effective August 2026. Non-compliance penalties reach 7% of global annual revenue. PYRAMYD's citation hierarchy and audit trails are built to satisfy the requirement.
Performance is part of the moat. 3.08× query speedup live (10-query test 2,519ms → 818ms) via pinned-transaction architecture.
See APEX answer your hardest competitive question.
Book a 30-minute demo. We'll run a multi-hop question against your category and show every citation.