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PYRAMYD

Product Intelligence Maturity Model

Five stages from spreadsheets to graph-grounded operations.

Every B2B SaaS team eventually walks this same path. Some skip stages by adopting the right substrate early. Some get stuck for years between stages because they migrated to the wrong architecture. Here's the map · and the honest version of what each stage costs.

5 stagesSkip-ahead paths includedBuilt on real customer engagementsNo vendor-evangelism stage 6
Diagnose your stageRead the AI Context use case

Why we're publishing this

Maturity models from vendors are usually thinly-disguised sales decks · stage 5 always means “buy our product.” This one names the stage where most teams actually live, the stage where most teams should aim, and the cost of getting between them. We say where PYRAMYD fits and where it doesn't.

The five stages

Each stage, named honestly.

01

Spreadsheets + Search

Stage 01

Where you are

Most pre-Series-B teams. PMM keeps a Google Sheet of competitors; AEs DM the PMM mid-deal; RFPs answered from a hand-curated Notion doc.

What hurts

  • Battle cards stale by mid-quarter; nobody trusts the spreadsheet
  • Same competitor question asked 5 times a week with no canonical answer
  • RFPs eat 25-40 hours each; SMEs interrupted constantly
  • Win/loss data lives in CRM notes; nobody analyzes the pattern

How to move to the next stage

Add a CI tool (Klue/Crayon) or RFP tool (Loopio) to consolidate the artifacts.

02

Point Tools, Disconnected

Stage 02

Where you are

Common for Series B-C. Klue + Loopio + Productboard each own a slice of the workflow. No tool sees the others' data.

What hurts

  • Three tools = three opinions on the same competitor
  • Battle cards refresh on PMM schedule, not market schedule
  • RFP answers cite a library, not the live product · go stale every release
  • Roadmap planning happens without live competitive context

How to move to the next stage

Bring the data together · either build a central data layer in-house, or adopt a graph-grounded platform that already has one.

03

Centralized Data, Document-Grounded AI

Stage 03

Where you are

Mid-market teams who've stood up a data warehouse + a vector-RAG copilot over a document corpus. The first real AI investment.

What hurts

  • Vector retrieval ranks chunks by similarity, not by entity relationship
  • Multi-hop questions (vendor × job × deal stage) break down
  • Citations point to documents, not to specific facts · audit fails
  • Re-embedding cost grows linearly with corpus size

How to move to the next stage

Move from chunk-based RAG to entity-based Graph RAG. The retrieval substrate is the thing that has to change.

04

Graph-Grounded Operations

Stage 04

Where you are

Where PYRAMYD customers operate. 90 universal node types, 249K+ products in the graph. APEX answers cite typed entities + source URLs.

What hurts

  • Schema evolution requires governance · whose taxonomy is canonical?
  • Live signal sources need entity resolution · fuzzy matching at scale
  • Tenant isolation has to be enforced at the graph layer, not just app layer
  • The team has to shift from data-engineering questions to graph-stewardship questions

How to move to the next stage

Expose the graph to downstream AI agents via MCP. Open the substrate to your internal copilots.

05

Agentic + Audit-Ready

Stage 05

Where you are

The destination. Internal copilots (Claude, Cursor, custom agents) ground answers via PYRAMYD's MCP server. Every response carries per-claim provenance for EU AI Act Article 50 and ISO/IEC 42001 audits.

What hurts

  • Procurement asks: 'where did this number come from?' · the audit log answers
  • Legal asks: 'is this AI-disclosed?' · the JSON-LD provenance block answers
  • Sales asks: 'what changed on the competitor this week?' · the live graph answers
  • Nothing about the substrate gets re-platformed when the next model generation ships

How to move to the next stage

Sustained operation. The maturity model isn't a one-time migration; it's an ongoing discipline.

Skip-ahead paths

You don't have to walk every stage.

The biggest mistake teams make is migrating between adjacent stages instead of skipping ahead. Here are the three paths we see most often.

Stage 01 → 04

Pre-Series-B teams often skip stages 02 and 03 entirely by starting with a graph-grounded platform. The lesson from data warehousing applies here · adopt the right substrate first, don't migrate twice.

Stage 02 → 04

Teams already on Klue/Loopio/Productboard who realize the point-tool stack is hitting its ceiling. The migration playbook is a 30-day cutover · see the migration guides under Resources.

Stage 03 → 04

Teams who built a vector-RAG copilot and ran into multi-hop failures. The fix is changing the retrieval substrate · the rest of the stack (data warehouse, LLM, MCP clients) stays.

Where PYRAMYD fits (and doesn't)

Honest scoping.

PYRAMYD is the right fit when

  • You're at stage 02 or 03 and the substrate is the bottleneck
  • You sell B2B software (the live graph is enterprise-software-specific)
  • You need audit-ready answers (EU AI Act, SOC 2, procurement security questionnaires)
  • You want one platform across CI + Product Ops + RFX + AI grounding

PYRAMYD is the wrong fit when

  • You sell consumer products (the graph schema is wrong shape for B2C)
  • You need a general-purpose enterprise knowledge graph · use Graphwise, Stardog, or Neo4j
  • You're at stage 01 and the lift to stage 04 isn't the right cost right now
  • You're already at stage 05 with an in-house graph platform you trust

Want a 30-minute stage diagnostic?

Walk through your current stack, the pain points your team is feeling, and which stage you're actually at · not where the vendor pitch deck says you are. We'll tell you what we can and can't help with.

Book the diagnosticSee the migration playbooks