Product Intelligence Maturity Model

Five stages from spreadsheets to graph-grounded operations.

Every software 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 stage Read 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.

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

Battlecards stale by mid-quarter; nobody trusts the spreadsheet
Same competitor question asked 5 times a week with no canonical answer
RFPs take days each; subject experts are 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.

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
Battlecards refresh on PMM schedule, not market schedule
RFP answers cite a library, not the current product, and go stale every release
Roadmap planning happens without current 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.

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 how records relate
Questions that join several records (vendor × role × 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

Connect your product's data to the market, competitors, customers, and deals in one place, so answers come from how the records relate, not from document chunks.

Graph-Grounded Operations

Stage 04

Where you are

Where PYRAMYD customers operate. 99 universal node types, 250K+ products in the graph. APEX answers cite typed nodes and source URLs.

What hurts

Schema evolution requires governance · whose taxonomy is canonical?
New signals need matching to the right company, at scale
Each organization's data has to be kept separate at the graph layer, not just the app layer
The team has to shift from data-engineering questions to graph-stewardship questions

How to move to the next stage

Bring the graph to the AI tools your team already uses: an early, token-based connection for Claude, Cursor, ChatGPT, and Microsoft 365 Copilot is in Settings today.

Agentic + Audit-Ready

Stage 05

Where you are

The destination. Your team's AI tools ground their answers in the same typed graph, with the sources behind each claim. An early version works today: a token from Settings connects Claude Desktop, Cursor, ChatGPT, or Microsoft 365 Copilot to your battlecards, competitors, and signals. Sign-in with your PYRAMYD login is coming.

What hurts

Procurement asks: 'where did this number come from?' — the source on the record answers
Legal asks: 'is this AI-generated?' — images are marked today, and text answers will carry the same disclosure once it ships
Sales asks: 'what changed on the competitor this week?' — the signals in the graph answer
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 guides under Resources walk through the move.

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 software (PYRAMYD is built for software companies)
You need audit-ready answers (EU AI Act, SOC 2, procurement security questionnaires)
You want one platform for your product, marketing, and sales teams

PYRAMYD is the wrong fit when

You sell consumer products (the graph schema is wrong shape for B2C)
You need a graph database for your own data: 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 diagnostic See the migration playbooks