01 · Four tools, four answers
Klue + Loopio + Productboard + a copilot.
Four contracts, four data silos, four refresh cadences, four search bars. PMM, Sales, RevOps, and Product all reach for the same competitor question and get different answers.
LIVE · releaseSalesforce shipped Agentforce 3.0 · pricing diff +12%
The product graph for enterprise software teams
Every competitor.Every category.Every signal.All in one live Graph.
620M+ data points252K+ products · 2,606 categories2.4M+ reviews · 1K+ live signal sources88 universal node types · 1,554 FKs
Loading 16,600-node production slice…
Pre-populated with 0K+ products across 0.0K categories, 0.0M+ reviews, and 0K+ live signal sources · so your Competitive Intelligence teams stop rebuilding the same intelligence.
200+ pre-built connectors
Browse the full catalogWhy grounding matters
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. The fix is grounding.
Gartner press release · June 25, 2025 · Anushree Verma, Senior Director Analyst
40%+
Agentic projects cancelled by 2027 (Gartner)
85%
AI projects fail to deliver business value (Gartner 2023)
73%
AI pilots never reach production (MIT Sloan 2024)
Act I · The problem
The old stack ships answers your team can't defend.
Four point tools. Four sources of truth. Each one ranks chunks instead of resolving entities. Sales gets answers; procurement asks for the receipt; nobody can show the chain. That's the structural failure agentic AI is now exposing.
02 · Vector retrieval ranks chunks
Document-grounded AI hallucinates the join.
Cosine similarity over text chunks works for FAQs. It collapses the moment a question crosses two entity types (vendor × persona × deal stage). And it cites the document, not the claim.
03 · Procurement is now asking
EU AI Act Article 50 took effect Aug 2, 2026.
Per-claim provenance and machine-readable AI-content marking are now table stakes for enterprise AI procurement. Document-RAG can't produce either. Graph-grounded answers ship both by default.
Act II · The bundle
Stop renting four tools. Own the graph.
Most teams pay $640K·$1.4M/yr across four point tools that don't share a graph. PYRAMYD replaces that stack with one subscription, one source of truth, and a copilot that cites every answer.
| Your current stack | Annual cost | PYRAMYD replaces with |
|---|---|---|
| Klue / Crayon — Competitive Intel | $50K·$100K | CI module · live categories, weekly battle cards |
| Loopio / Responsive — RFP / RFX | $30K·$80K | RFX module · graph-grounded answer composition + audit-ready citations |
| Productboard / Pendo — Product Ops | $60K·$200K | Product Ops module · Feature Market Fit + competitor releases |
| Neo4j + Data team — Build-your-own KG | $500K+ · 12·24 mo | Pre-built, MCP-ready graph · live in days, not quarters |
| Total | $640K·$1.4M / yr | From $50K / yr |
Annual cost ranges from public ACV benchmarks and analyst commentary on each category (Klue/Crayon, Loopio, Productboard, Neo4j) · All amounts USD · Build-your-own estimate includes engineering + data acquisition over 12·24 months.
Act III · The substrate
One platform. Four layers. The PYRAMYD itself.
The PYRAMYD logo is our architecture. Click any band of the PYRAMYD to see what powers it · from the Data Engine at the base to the APEX copilot at the tip.
Layer 04 · APEX
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 21 specialist agents (Battlecards, RFX, Win/Loss, Pricing, Releases, Personas, …) through one supervisor · so users ask one question and the right specialist responds.
- 21 specialist agents routed by intent and skill
- ~165 graph-grounded tools · every tool reads from typed nodes, not raw text
- MCP-native · plug APEX into Claude / ChatGPT / Cursor / any MCP client
- Per-answer citation set with source URL, model, and prompt hash
21
specialist agents · ~165 graph tools
Act IV · The workspace
Nine tabs. One graph. One Smart Action menu.
Studio is the workspace humans actually open in the morning · nine surfaces that all share the same live graph, the same provenance, and the same APEX copilot.
01 · Dashboards
Pipeline by stage
Win rate
62%
+8 vs. Q1
Signal feed
Salesforce shipped Agentforce 3.0
HubSpot raised $200M · 28× ARR
Crayon hiring 14 engineers
Act V · The mechanism
A single packet of intelligence, flowing up the stack.
Watch a signal travel from a connector through each layer of the PYRAMYD · arriving at the user as a cited, audit-trail-backed answer in seconds.
Connector
Data
AI Models
Studio
APEX
Answer
Layer 01 · Data Engine
1,554 FK constraints · 88 node types · graph hydration
Layer 02 · AI Models
Embeddings · provider routing · token + cost audit
Layer 03 · Studio
Dashboards · Sheets · Documents · Slides · Agents
Layer 04 · APEX
Specialist agent routing · citation assembly · MCP egress
Built on Graph RAG, not Vector RAG. Built on real product data, not scraped text. Built for enterprise software teams, not generalists.
Act VI · The workflows
Built for the four teams that win on intelligence.
Each use case shares the same graph, the same copilot, and the same source of truth · so what one team learns, every team can act on.
Before you keep scrolling
Bring one hard competitive question. We'll show you the answer in your category.
30 minutes. No slideware. We pull your top 5 competitors into a live graph slice before the call, then walk APEX through a real multi-hop question with the citations live.
Act VII · The proof
A typed graph, not a list of products.
88 universal node types stitched together by 1,554 FK constraints. Pre-populated with 252K+ products, 2,606 categories, 2.4M+ reviews, and 1,000+ live signal sources · refreshed weekly. The substrate is the moat; the volume is the proof.
252K
enterprise software products
Across 2,606 categories, with 2.4M+ aggregated reviews and 88 universal node types linking them.
1,000+
continuously refreshed signal sources
200+ pre-transformed connectors, 183 bi-directional flows. Multi-tiered refresh tuned to release cadence.
Graph RAG
inline embeddings · multi-hop
Vertical AI grounded in typed entities and citation hierarchy · not a horizontal copilot reading the open web.
Act VIII · The contract
Trusted by procurement. Priced for teams.
Enterprise-grade compliance from day one. Per-seat pricing where the whole organization receives the feeds, digests, and battlecards.
Security & Trust
See how we keep your data yours.
Row-level security, tenant-isolated graph slices, audit-ready AI answers · every certification we're pursuing, in progress today.
- SOC 2 Type 2In progress
- ISO 27001In progress
- ISO 42001In progress
- GDPRIn progress
- CCPAIn progress
- AWS Well-ArchitectedIn progress
- EU AI Act · Article 50Ready
Pricing
Simple plans that grow with your team.
Per-seat pricing from $500/seat/mo (Starter, annual) to $1,200/seat/mo (Growth, annual) · Enterprise volume pricing starts ~$2,500/seat/mo at 50+ seats. Monthly or annual billing, save 20% annually. Every plan includes the full graph, the workspace modules, and citation-backed APEX answers.
See pricingAct IX · Why now
The market is moving · here's what's independently measurable.
Independent research from Gartner, MIT Sloan, and Loopio · the shifts everyone shipping enterprise AI is now planning around. No vendor self-reports.
40%+
of agentic AI projects will be cancelled by end of 2027 · escalating costs, unclear value, inadequate risk controls.
Gartner press release · June 25, 2025 · Anushree Verma, Sr Director Analyst
73%
of enterprise AI pilots never reach production deployment. Grounding and integration are the dominant failure modes.
MIT Sloan Management Review · 2024 enterprise AI pilot survey
$725K
median annual revenue lost to unfinished RFPs per organization · $875K at enterprise scale, $1.05B in healthcare.
Loopio 2026 RFP Trends & Benchmarks Report · n=1,533 response professionals
Citations linked to primary sources. PYRAMYD doesn't fabricate third-party stats · if a number isn't verifiable, it's reframed qualitatively or removed.