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PYRAMYD

APEX Copilot

Agentic AI, grounded.

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.

APEX Copilot answering: which competitors shipped voice-AI in the last 90 days? — captured live from app.pyramyd.ai
Live from app.pyramyd.ai · /industry-360Every claim links to source

The agent swarm

One supervisor. 21 specialist agents. ~165 graph tools.

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.

APEXSupervisorBattlecardWin/LossPricingReleasesReviewsPersonasCategoryIndustryCountryFundingTalentRFXICPSegmentEcosystemComplianceWebsite DiffAlertsM&AFeatureTrend

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

Question to cited answer in 5 stages.

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.

  1. 01 · Ask

    User asks APEX

    Which 3 CRM vendors are most threatened by AI-native entrants?

  2. 02 · Route

    Supervisor agent routes intent

    Picks 2 specialists: Competitive analyst + Innovation watcher.

  3. 03 · Retrieve

    Multi-hop graph traversal

    Product Categories (CRM) → Market Leaders → cross-ref with Funding Signals + AI Disruption score

  4. 04 · Cite

    Citations assembled

    8 source URLs · 3 review aggregates · 1 analyst quote · all returned with prompt + model + token hash

  5. 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

Four capabilities that keep agentic AI projects alive.

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

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.

02 · Auditable

Every answer carries provenance and a citation trail.

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

Connect your internal copilots through the MCP server.

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

Row-level security on every read and write.

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

Same question. Different answers. Different evidence.

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:

  1. 1. Wiz · 1,842 reviews [g2.com, ↗]
  2. 2. Palo Alto Prisma Cloud · 1,514 reviews [g2.com, ↗]
  3. 3. Lacework · 1,128 reviews [trustradius, ↗]
  4. 4. Orca Security · 904 reviews [g2.com, ↗]
  5. 5. Sysdig Secure · 612 reviews [g2.com, ↗]

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:

  • Wiz · widely regarded as a leader
  • Palo Alto Prisma Cloud
  • Lacework
  • Orca Security
  • CrowdStrike Falcon Cloud Security

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

Vector RAG retrieves chunks. APEX traverses entities.

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)

Document chunks · cosine similarity · single hop

  • Retrieval unit: Text chunk · no entity boundary
  • Multi-hop reasoning: Weak · cosine similarity over chunks doesn't follow relationships
  • Citation granularity: Document-level · “according to this PDF”
  • Update cycle: Re-embed on every source change · expensive at scale
  • Failure mode: Hallucinates the join · “trust me, these chunks are related”

APEX Graph RAG

Typed nodes · FK traversal · multi-hop with provenance

  • Retrieval unit: Typed entity (Product, Vendor, Category, Persona, Country)
  • Multi-hop reasoning: Native · FK constraints traverse relationships across entity types
  • Citation granularity: Cell-level · source URL + retrieval timestamp on each claim
  • Update cycle: FK constraint propagates the change · no re-embedding required
  • Failure mode: Returns “no path found” instead of hallucinating

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.

Ground your agents in the same graph our customers use.

Book a demo to see APEX answer your hardest competitor, category, and customer questions · with full provenance, in seconds.