S&P built a $140B business scoring creditworthiness. We score structural competitive advantage. Every engagement, every night, every vertical makes the system smarter. The database is the moat.
Brand ecosystems are complex systems with value flows, authority structures, and loyalty mechanisms. The entire marketing industry optimizes inside these systems without diagnosing whether the system itself is broken. We are the blood test.
Two years ago, a single structural analysis cost $700+ in compute. Quantized local LLMs dropped that to $7. The billion-node knowledge graph is now economically feasible for the first time.
Scott Galloway built L2 into a $100M+ business scoring digital brand competence. Gartner acquired it. Nobody has built the successor with modern AI infrastructure. The market window is open.
Everyone assumes AI commoditizes brand consulting. The opposite is true: AI makes the right kind of brand consulting a compounding monopoly, because domain-specific knowledge graphs improve with every engagement while generic LLMs cannot.
Emerging categories (micro-drama entertainment, AI agent infrastructure) are consolidating in months, not years. Brands in fast-moving verticals need structural intelligence now. First-mover advantage is measured in weeks.
| Client | Sector | Outcome |
|---|---|---|
| American Heart Assoc. | Healthcare Nonprofit | Brand power score, viz suite, 5/5 satisfaction — asked for 3 more verticals |
| Fiserv | Fintech (Fortune 500) | Brand diagnosis (46/100 Distressed) led to $40K naming follow-on |
| FrameBright | Child Safety Tech | Full pipeline: 10 gaps, value prop canvas, active engagement |
| MicroCo | Entertainment | Weekly intelligence for 22 companies, ongoing SBPI scoring |
| Deckers Brands | Consumer (CPG) | Portfolio gap analysis (HOKA 75, UGG 64), C-suite showcase |
| Long Zhu | Education/Gaming | Layered ontology, BMC overlay, value flow analysis |
| AFDVI | Nonprofit | Donation strategy, $100K-$500K revenue opportunity mapped |
Built in 90 days by a 4-person team. Every deliverable is live on the web, not mockups. The pipeline produced this. Now we scale it.
| Tier | What | Timeline | Price |
|---|---|---|---|
| Tier 1 | Outside-in structural diagnosis using public data | 3–5 weeks | $40K–$75K |
| Tier 2 | Validates gaps with internal data (CAC, LTV, retention) | 6–10 weeks | $90K–$150K |
| Tier 3 | System-level design that repairs value flows | 8–12 weeks | $125K–$250K |
| Monitoring | Quarterly structural drift detection + persistent scoring | Ongoing | $8K–$20K/mo |
The client gets a paid deliverable (the Artifact). SHUR IQ gets structured knowledge deposited into a proprietary graph (the Asset). Revenue on the front end. Compounding IP on the back end.
The autoresearch pipeline produces the same structural analysis as a $40K client engagement for $7 in compute. It runs every night on locally hosted models. At scale: 50,000+ reports per year for $350K total compute — the same intelligence output that would cost a competitor millions in analyst time.
The $7 auto-research loop decouples knowledge graph growth from the client sales cycle. The database grows every night at 6:13 AM whether we have clients that week or not. A competitor who starts later starts with an empty database.
The proprietary knowledge graph tracks 1,694 companies across 2 verticals with 76,000+ structured facts. No public dataset encodes structural brand dynamics. This schema has no equivalent in Wikidata, Schema.org, or any LLM’s training data.
| IP Variable | What It Measures | Current Value |
|---|---|---|
| Node Density | Entities and relationships per analysis | 76,147 structured facts, 1,694 companies |
| Schema Uniqueness | Coverage of domains no public dataset models | 12 classes, 22 properties — zero public equivalent |
| Extraction Efficiency | Cost to move a fact from unstructured to structured | $7 (auto) to $40K (client-grade) |
| Temporal Depth | Weeks of scoring history per company | Growing weekly — historical trajectories ARE the product |
These four variables are trackable by investors. Node Density grows with every engagement and every nightly cycle. Schema Uniqueness deepens with every new vertical. Extraction Efficiency improves with every experiment. Temporal Depth accumulates automatically. A competitor cannot buy time.
ReelShort, DramaBox, Netflix, Disney, Amazon, and 17 more. Full structural scoring with week-over-week deltas and published rankings.
Y Combinator batch analysis. Same scoring methodology. Same nightly cycle. Same knowledge graph substrate. Applied in 2–3 days.
No new code. No new infrastructure. Company list and dimension weight calibration are the only per-vertical inputs. Each new vertical adds to the shared graph — the system gets smarter at analyzing ANY vertical because it has already learned the structural behavior of markets.
Client engagement =
Revenue ($40K–$250K)
+ KG Deposit (structured facts)
Auto-research =
$7 compute
+ KG Deposit (same extraction)
A consulting firm’s value is its people. SHUR IQ’s value is its people plus a growing, queryable database of structural intelligence. The competitor who starts later does not just start behind — they start with an empty database in a domain where historical trajectories are the product.
McKinsey, BCG, Bain, Gartner. Human analysts, static reports, $500K+ per engagement. No data asset. No compounding. Knowledge walks out the door.
No established player combines human expertise with compounding machine intelligence for brand diagnostics.
Brandwatch, Sprout Social, Talkwalker. Surface metrics (sentiment, impressions, share of voice). No structural diagnosis. No competitive architecture.
Expert-designed ontology + machine scale + compounding data asset. Every engagement and every nightly cycle widens the moat. The only system where the intelligence improves itself.
L2 (acquired by Gartner) is the closest precedent. Galloway built a $100M+ business scoring digital brand competence with human researchers. We do it with autonomous agents and a knowledge graph that compounds. L2 never had that.
| Tier | Volume | Revenue |
|---|---|---|
| Tier 1 | 6 | $360K |
| Tier 2 | 4 | $480K |
| Tier 3 | 3 | $540K |
| Monitoring | 3 | $540K |
| Target | $1.92M |
Conservative: $1.38M (5/3/2/2). Gross margin: 55–70%. Core team: 3–5 operators.
Service revenue grows linearly with headcount and sales effort. The knowledge graph asset grows exponentially through the $7 auto-research flywheel.
By Year 2–3, the cumulative value of the knowledge graph — the structured, expert-validated intelligence across dozens of verticals — begins to exceed cumulative service revenue.
At that inflection point, SHUR IQ transitions from “a consulting firm with good tools” to “an infrastructure asset with a funded acquisition channel.”
Post-critical-mass: subscription access to the SHUR IQ Atlas. Clients query the validated knowledge graph for internal simulations. Bloomberg Terminal economics for structural brand intelligence.
This entire portfolio — 7+ engagements, 15+ deployed sites, 76K structured facts, nightly automation, weekly publications — was produced by four people. Headcount scales linearly. The knowledge graph scales exponentially.
Every week of scoring data is a week of competitive history that did not exist before we created it. A competitor who raises $50M next year still starts with zero weeks of structural brand trajectories. Our database is not just bigger — it is older. In structural intelligence, time is the moat.
| Category | Allocation | What It Builds |
|---|---|---|
| Infrastructure | 40% | Local LLMs, graph databases — the IP factory that runs 50K+ reports/year at $7 each. Machine CapEx. |
| R&D / Agentic Tuning | 30% | Auto-research pipeline optimization, cross-vertical transfer R&D, accuracy experiments targeting 85%+ |
| Expert Calibration | 20% | Domain expert validation of the knowledge graph — the human layer that separates verified intelligence from LLM noise. Human CapEx. |
| Sales & GTM | 10% | Channel validation, stack ranking publication, beta diagnostics. The public ranking IS the marketing (L2 model). |
Capital does not pay for consultants. It builds infrastructure that lowers the marginal cost of intelligence toward zero. Every dollar invested accelerates the flywheel. 70% goes to machines and ontology engineering. 30% goes to the humans who make the output expert-grade.
The knowledge graph exists. The clients exist. The traction exists. The autoresearch engine improves itself every night. Investment accelerates distribution across verticals and markets.
Limore Shur — limore@weareshur.com
Nuri Djavit — ndjavit@weareshur.com
Shur Creative Partners — Confidential — March 2026
Production intelligence products built by a 4-person team. Not mockups. (Links open in new tabs)
Use-of-Funds Model: Google Sheets