The Platform

How every answer stays honest.

Ask a question, get an answer you can put in front of your CEO — because every claim traces back to the source it came from. Four layers make that possible: the Data Foundation holds the facts, the AI Models read them, Studio lets you build on them, and APEX answers in plain language — with citations, or not at all.

Book a demo See the data foundation

Four Layers

The PYRAMYD is the architecture.

Click any band of the PYRAMYD to expand. Each layer is independently powerful — together they're one system: one workspace and one copilot on one graph.

Layer 04

APEX

The supervisor copilot on top of the entire stack.

APEX is the citation-grounded copilot that sits on top of the AI Models layer and reasons over the live graph. It orchestrates 20 specialist agents (Battlecards, RFX, Win/Loss, Pricing, Releases, Personas, …) through one supervisor — so users ask one question and the right specialist responds.

20 specialist agents routed by intent and skill
150+ tools across the specialist agents
MCP access for ChatGPT, Claude and Cursor · coming soon
Per-answer citation set with source URL, model, and prompt hash

specialist agents · 150+ tools

Built on a typed graph, with vector search as a helper. Built on real product data, not scraped text. Built for enterprise software teams, not generalists.

One graph instead of four tools

One workspace instead of four separate tools.

Competitive intelligence, product operations, sales enablement and RFPs, and AI context are usually four products, often bought by the same person. In PYRAMYD they run on one graph.

One workspace, one copilot, one source of truth — across the four jobs your teams already do.

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 a knowledge base of the enterprise software market, pre-populated before you sign in.

The Architectural Advantage

Graph RAG vs vector retrieval · structurally different substrates.

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.

Retrieval unit: Text chunk, ranked by similarity

Graph RAG (PYRAMYD)

Typed nodes and edges. Multi-hop reasoning with citations.

Retrieval unit: Typed graph node (Product, Vendor, Category, Role, Country)

Benchmark Comparison

Multi-hop reasoning
Vector
Weak · cosine similarity on chunks
Graph
Native · traversal across node types
Lift:
Citation granularity
Vector
Document-level
Graph
Cell-level · source URL + timestamp per claim
Lift:

Multi-hop reasoning	Weak · cosine similarity on chunks	Native · traversal across node types
Citation granularity	Document-level	Cell-level · source URL + timestamp per claim

PYRAMYD's stack: Mastra Graph RAG + CopilotKit UI + 91-node universal schema + about 200 connectors + 2,000+ signal sources.
Background · Microsoft Research GraphRAG (arXiv:2404.16130)

Multi-hop reasoning

Every hop is typed. Every claim is cite-able.

Watch APEX traverse the graph: from a Category, through its top Products, to their Reviews, to the underlying Signals — assembling a cited answer in 4 hops. A vector store can't do this — it retrieves similar text, not connected entities.

Category
Product · Vendor
Review aggregates
Live signals (funding, talent, releases)

Hop 0 / 5

Gartner's 2026 Hype Cycle for Agentic AI shows 3-4× accuracy lift on multi-hop queries when retrieval is graph-typed instead of vector-only. Every hop here is a real FK constraint in the production graph.

How it works

Four stages. One graph.

From a vendor's release note hitting the ingestion layer to APEX citing it in a sales-rep briefing — the path through the platform, animated.

G2TrustRadiusGetAppCapterraSoftware AdviceProduct HuntGartner Peer InsightsIT Central StationFeaturedCustomersCrowdReviewsVendor websitesTechnical documentationChangelogsAPI docsStatus pagesPress releasesBlog feedsInvestor relations pagesTechCrunchThe InformationArs TechnicaVentureBeatProtocolStratecheryAxios ProBusinessWirePRNewswireGlobeNewswireReutersBloombergSEC EDGAREU Commission filingsUSPTO PatentsWIPO IP databaseCrunchbaseCB InsightsFedRAMP marketplaceFINRA filingsLinkedIn jobsGreenhouse boardsLever boardsWorkable boardsIndeedAngelList TalentBuilt InGlassdoorSalesforce AppExchangeHubSpot EcosystemSlack App DirectoryAtlassian MarketplaceShopify App StoreAWS MarketplaceAzure MarketplaceGoogle Cloud MarketplaceZoomInfo dataReddit (r/SaaS, r/devops, r/cscareers)Hacker NewsTwitter / XLinkedIn PulseIndustry RSS aggregatorVertical newslettersSubstack techSOC 2 attestationsISO 27001 registerHIPAA disclosuresEU AI Act Article 50 disclosuresG2TrustRadiusGetAppCapterraSoftware AdviceProduct HuntGartner Peer InsightsIT Central StationFeaturedCustomersCrowdReviewsVendor websitesTechnical documentationChangelogsAPI docsStatus pagesPress releasesBlog feedsInvestor relations pagesTechCrunchThe InformationArs TechnicaVentureBeatProtocolStratecheryAxios ProBusinessWirePRNewswireGlobeNewswireReutersBloombergSEC EDGAREU Commission filingsUSPTO PatentsWIPO IP databaseCrunchbaseCB InsightsFedRAMP marketplaceFINRA filingsLinkedIn jobsGreenhouse boardsLever boardsWorkable boardsIndeedAngelList TalentBuilt InGlassdoorSalesforce AppExchangeHubSpot EcosystemSlack App DirectoryAtlassian MarketplaceShopify App StoreAWS MarketplaceAzure MarketplaceGoogle Cloud MarketplaceZoomInfo dataReddit (r/SaaS, r/devops, r/cscareers)Hacker NewsTwitter / XLinkedIn PulseIndustry RSS aggregatorVertical newslettersSubstack techSOC 2 attestationsISO 27001 registerHIPAA disclosuresEU AI Act Article 50 disclosures

Built Enterprise-Grade from Day One

Four product layers, audit-ready by design.

Data Foundation, AI Models, Studio, and APEX — all running on Azure with row-level security, tenant-isolated graph slices, embeddings on the core tables and a signal pipeline.

Architecture

Compliance

SOC 2 Type 2In progress
ISO 27001In progress
ISO 42001In progress
GDPRIn progress
CCPAIn progress
EU AI Act Article 50In progress

EU AI Act Article 50

Article 50 has applied since 2 August 2026. Breaches can be fined up to EUR 15 million or 3% of worldwide annual turnover. Generated images already carry a machine-readable AI marker; disclosure and marking for text answers are being built.

Performance is part of the foundation. 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.

Book a demo Review the security posture