01 · Graph-grounded
Answers grounded in 250K+ products and 1K+ signal sources.
APEX queries the live Product Graph · not the open web, not a static document index. Every claim links back to a typed node and a source URL.
APEX Copilot
Every answer cites the underlying node. Every citation traces to a source URL.
APEX is the copilot layer of the Product Graph · built on Graph RAG, not vector retrieval. It answers competitor, category, and customer questions with full provenance, so the answer survives an audit.

The agent swarm
APEX isn't one model · it's a coordinated swarm. The supervisor reads your intent and routes the right specialists. Watch a query fan out, light up the relevant agents, and assemble a cited answer.
You ask APEX
Build me a battle card for HubSpot vs. Salesforce in mid-market.
APEX returns
Waiting for specialist responses…
One supervisor coordinates 21 specialist agents, each with its own graph-grounded toolset (~165 tools total). The supervisor picks the right specialists per query · you ask one question, the swarm responds.
Anatomy of an APEX answer
Every APEX call follows the same lifecycle · whether you ask in Studio, in Slack, or from an MCP-connected client. Watch a real query travel from prompt to citation.
01 · Ask
User asks APEX
Which 3 CRM vendors are most threatened by AI-native entrants?
02 · Route
Supervisor agent routes intent
Picks 2 specialists: Competitive analyst + Innovation watcher.
03 · Retrieve
Multi-hop graph traversal
Product Categories (CRM) → Market Leaders → cross-ref with Funding Signals + AI Disruption score
04 · Cite
Citations assembled
8 source URLs · 3 review aggregates · 1 analyst quote · all returned with prompt + model + token hash
05 · Answer
Cited answer streamed
1. Salesforce · 2. HubSpot · 3. Microsoft Dynamics · with link to every claim.
The same lifecycle runs for every APEX call · in Studio, in Slack, and over MCP from any external client. Every citation traces back to a typed node on the graph; every prompt is hashed + auditable.
What APEX does
Gartner predicts over 40% of agentic AI projects will be cancelled by end of 2027 due to escalating costs, unclear business value, and inadequate risk controls (Gartner press release, June 25, 2025). APEX is the grounding layer those projects need to survive.
01 · Graph-grounded
APEX queries the live Product Graph · not the open web, not a static document index. Every claim links back to a typed node and a source URL.
02 · Auditable
Entity-level citations on every response. Hover any claim to see the underlying signal, source, and timestamp · ready for SOC 2 and EU AI Act audit logs.
03 · MCP-native
APEX ships with a Model Context Protocol server, so your existing AI workflows (Claude, ChatGPT, internal agents) can ground answers in the same graph.
04 · Tenant-isolated
Your tenant's slice of the graph (battle cards, RFX responses, win/loss data) stays private. Public graph data improves through aggregate trends only.
APEX vs. a general-purpose LLM
A real prompt run against APEX (grounded in the Product Graph) and ChatGPT (general web knowledge). Note the citation count, recency, and specificity.
Prompt
“Who are the top 5 CSPM vendors with FedRAMP authorization and Salesforce integration, ranked by review count?”
APEX
Graph-grounded · 1.4s
Here are the 5 vendors meeting all three criteria, ranked by aggregated review count:
Provenance
18 citations across 4 sources. All FedRAMP statuses verified against fedramp.gov (retrieved 2026-05-22). Salesforce integration confirmed via each vendor's integration directory.
ChatGPT
General LLM · 6.2s
Here are some leading CSPM vendors that may meet those criteria. Note that FedRAMP and integration details can change over time and I'd recommend verifying with the vendor:
I don't have specific review counts or up-to-date FedRAMP status for these. For ranking, you may want to consult G2 or Gartner directly.
Provenance
No citations. Training data cutoff ~2024-04. FedRAMP and integration status unverified.
18 → 0
Citations (APEX vs ChatGPT)
2026-05 → 2024-04
Data recency
4.4× faster
Time to grounded answer
Why Graph RAG
Most enterprise AI projects bolt a vector index onto a document corpus. That works for FAQs. It fails the moment a question requires joining two facts that live in different documents, or following a chain of relationships.
Vector RAG (what most teams ship)
APEX Graph RAG
The trade-off: Graph RAG requires the graph to exist. That's the foundation · PYRAMYD pre-built it across 88 universal node types, 1,554 FK constraints, and the verified Product Graph. Vector RAG ships faster · the graph is what makes the answers survive an audit.
Book a demo to see APEX answer your hardest competitor, category, and customer questions · with full provenance, in seconds.