A system for building connected knowledge graphs and ontology design layers that represent a business as a navigable, toggleable, traceable system.
Foundation
Doug Engelbart's Dynamic Improvement system operates on three concurrent teams. Every engagement activates all three.
This document is Team C output. It defines the system that Team B uses. Each client engagement is a Team B instance. The Long Zhu engagement is the first reference implementation.
Core Distinction
Every graph in the system is one of two types. Understanding the difference is the key to the entire architecture.
Descriptive. Represents observed reality and discovered structure. Created from source material through entity extraction and relationship mapping.
Empirical, structural, temporal, queryable. InfraNodus tools return factual answers about the graph's structure.
5 layers: K1–K5Prescriptive. Represents the framework by which knowledge graph entities are classified, evaluated, and acted upon. Designed by the analyst.
Normative, configurable, inherited, composable. Multiple layers can be active simultaneously.
4 layers: O1–O4The same entity appears differently depending on which layers are active. A pedagogical_framework node through a Market viewport asks "Is there demand?" Through a Social viewport: "Who on the team can build this?" Through a Temporal lens: "Is this Recipe, Plan, or Observation?"
Architecture
Each toggle answers a different question about the same entity. The combination of active toggles defines the analytical context.
"Should Long Zhu invest in organized play?"
K1 ON Base entities — K3 ON What resources does organized play require? — K5 ON What did Flesh and Blood and MetaZoo do?
O1 ON Organized play = Key Activity + Channel — O3 ON Recipe only, no Plan or Observation — O4 floor=0.7 Professional consensus: organized play is critical
Knowledge Graphs
Each knowledge layer is a distinct InfraNodus graph per project. Together they form the empirical foundation.
The base layer. Entity landscape extracted from all source material. Reveals clusters, gaps, bridges, centrality.
Structural gaps between clusters. Maps the absence of connections. What should be connected but isn't.
REA mapping: who provides what to whom, through what processes, under what constraints. Three sub-graphs: agents, resources, flows.
Five-dimension scoring: Awareness, Trust, Mission, Differentiation, Loyalty. Enables cross-project stack ranking within verticals.
Market positioning relative to competitors. SERP analysis, competitor landscape, differentiation gaps.
Long Zhu — K1 Business Intelligence
150 nodes, 276 edges, 16 clusters, 0.87 modularity. Extracted from 18-page seed round deck, project plan workstream doc, and Gantt timeline.
Top 5 clusters carry 95% of betweenness centrality. The community-building cluster (9% influence, 1% BC) is the most disconnected high-influence cluster. Revenue forecast and player commitment clusters have zero bridging to anything else — claims floating without structural support.
Long Zhu — K3 Value Flow
137 nodes, 335 edges, 12 clusters, 0.77 modularity. The "person" node has highest betweenness centrality (0.45). The business is deeply people-dependent.
| Agent | Role | Background |
|---|---|---|
| Kevin Mowrer | CEO + Game Design | Hasbro R&D, 20+ patents, Beast Wars, Dragon Booster |
| Limore Shur | Marketing + App Dev | Nike, Amazon, Best Buy, Target brand building |
| Steve Weinstein | Chief Creative | Mattel, Hasbro, Tonka product design |
| Julian Chan-Bevan | Creative Director | Netflix, Paramount, Universal brand strategy |
| Keith Bencher | Finance | — |
| Ben Mauceri | Legal / IP | — |
137 nodes, 319 edges, 12 clusters, 0.79 modularity. Funding allocation has 47% of betweenness centrality. The business is capital-constrained; every resource traces back to seed money allocation.
15 clusters, 0.69 modularity. meaningful_fun is the sole hub (bc: 0.55). Every value flow routes through the company. No distributed value creation exists.
Five primary value chains identified: Capital→Product→Revenue, Product→Distribution→Revenue, Product→Digital→Engagement, Marketing→Awareness→Conversion, Growth→Series A.
Ontology Design Layers
These are interpretive frameworks applied on top of knowledge graphs. They are not extracted from source material — they are designed by the analyst.
Maps every KG entity to one or more of 9 BMC blocks. Enables channel tracing: follow any entity from Key Resource through Revenue Stream.
Four analytical frames that filter the same data differently: Market, Social, Environmental, General Knowledge.
Three layers distinguishing when a statement is true. Recipe (possible), Plan (committed), Observation (actual). The gap between layers is where blindspots live.
Governs how much to trust each claim. Five tiers from Legal/Regulatory (0.9–1.0) through Personal (0.0–0.2). High-stakes decisions use a higher floor.
Long Zhu — O1 BMC Overlay
67 entities mapped across 9 BMC blocks. Each entity tagged with its temporal status.
Any entity can be followed through the BMC from its origin block to its downstream effects. If any link is missing, that's a gap.
Long Zhu — O3 Temporal Layer
The only flows with real-world evidence:
| Kevin's franchise track record | 0.95 |
| Team industry experience | 0.90 |
| Patents filed | 0.85 |
| Game mechanics in development | 0.75 |
| Concept testing occurred | 0.60 |
The highest execution risk. Items committed in the plan with timelines but zero evidence of execution:
| Planned Flow | What Would Make It Observation | Status |
|---|---|---|
| Raise $1.1M seed | Signed term sheets, money in bank | PLAN |
| 350 cards developed | Playable card set, playtesting data | PLAN |
| Battle Story App built | Working prototype, app store listing | PLAN |
| Distribution partnerships | Signed agreements with distributors | PLAN |
| Gen Con debut | Booth reserved, demos planned | PLAN |
| $150K Y1 revenue | Actual sales data | PLAN |
Long Zhu — Applied Analysis
Long Zhu / Meaningful Fun is a pre-launch educational TCG startup with an elite team (Hasbro, Nike, Netflix alumni), a novel product category, and a $1.1M seed round target. The layered ontology reveals the structural reality beneath the pitch.
Only 5 of 67 mapped entities have observation-layer evidence. The entire business model canvas operates on projected capabilities with near-zero validation. This is expected for a pre-launch startup. The critical question: which projections need validation before investors commit?
O1 + K3 Combined
Four structural breaks where the value flow chain is disconnected.
Channel Traces
Each go-to-market phase traced through the full BMC chain. Readiness = percentage of chain links that exist.
The business can execute Pre-Release and partial Debut with current resources. Everything from Launch onward requires significant capability building that isn't yet in the plan.
K3 + O1 + O3 Combined
For each critical resource: what happens downstream through the BMC if removed vs. added?
One unnamed person assigned to build 3 major app features. No CTO. No prototype. RECIPE
Zero allocation. Competitive scene mentioned in plan but unfunded. RECIPE
Listed as HR need. Nobody on team has education credentials. Core VP depends on this. RECIPE
Two-tranche SAFE. Not yet raised. Every downstream resource depends on this. PLAN
CEO + game designer + BD + investor relations + demo presenter. Hub node (bc: 0.55). OBSERVATION
Reproducible
How to build this system for any project. Prerequisites: K1 graph exists, source documents available, gap report exists.
Extract agents, resources, and flows from source documents into three InfraNodus graphs. Agents and resources use extractEntitiesOnly. Flows use none mode to preserve verb relationships.
For each entity in the value flow graphs, assign to one or more of 9 BMC blocks. Document gaps: which blocks are thin? Which chains are broken?
Tag every flow as Recipe (possible), Plan (committed), or Observation (actual). The gap between Plan and Observation is where execution risk lives.
For each major initiative, trace the full BMC chain: Key Resource → Key Activity → VP → Channel → Customer Segment → Revenue Stream. Missing links are gaps.
For each critical resource, model downstream effects through the BMC chain if added, removed, increased, decreased, or redirected.
Produce BMC overlay report, channel trace document, resource impact report, and update project INDEX. Optionally: BMC-annotated force graph visualization.