SHUR IQ encodes brand dynamics as structured knowledge. Every engagement, every week, 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 campaigns inside these systems without ever diagnosing whether the system itself is broken. Prescribing medication without running a blood test.
Every existing brand measurement tool produces static reports based on surveys and expert judgment. SBPI encodes the underlying structure as RDF triples with OWL-class semantics, making brand dynamics queryable and optimizable. The difference between opinion and structure is the difference between a PDF and a database.
| 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 drift detection, persistent knowledge graphs | Ongoing | $8K–$20K/mo |
Each tier deepens the engagement AND feeds the proprietary knowledge graph. The client pays for diagnosis. SHUR IQ gains a compounding data asset.
| Dominant | 85–100 |
| Strong | 70–84 |
| Emerging | 55–69 |
| Niche | 40–54 |
| Limited | <40 |
Every ScoreRecord carries dimension-level breakdowns, source attestations with confidence scores, and signal URLs. Scores are structured data with full provenance chains — queryable and auditable by any third party.
The ontology spans the full intelligence cycle: Company, ScoreRecord, DimensionScore, Signal, Attestation, Prediction, ImpactReport, Recommendation, and SignalWeightConfig. A competitor who starts today starts with an empty graph.
The system that runs tonight is worse than the system that runs next month. Every experiment is grounded in a specific published paper with explicit parameter mappings. Goodhart guard, multi-objective optimization, dimension weight learning, temporal decay, cross-vertical transfer.
ReelShort, DramaBox, Netflix, Disney, Amazon, and 17 more. Full SBPI scoring with week-over-week deltas.
Y Combinator batch analysis. Same RDF/SPARQL substrate. Same nightly cycle. Same OWL ontology.
Adding a new vertical takes 2-3 days of configuration, zero infrastructure cost. The only per-vertical inputs are company lists and dimension weight calibration. The shared ontology enriches with every vertical added.
Each engagement deposits structured facts into the knowledge graph. Gap patterns from Fiserv improve analysis of FrameBright. Cross-client intelligence without sharing client data.
Every week of scoring data trains the autoresearch pipeline. More weeks = better parameters = higher accuracy. The system that ran in March is measurably worse than the system running in June.
Engagements generate revenue AND knowledge graph triples. Monitoring retainers generate recurring revenue AND longitudinal data. The IP appreciates independently of headcount.
A consulting firm’s value is its people. SHUR IQ’s value is its people PLUS a growing, queryable database of structural brand intelligence. The competitor who starts later starts with an empty database.
| Tier | Offering | Price | Conversion |
|---|---|---|---|
| Tier 1 | Signal Intelligence (no internal access required) | $40K–$75K | Entry layer |
| Tier 2 | Deep Dive (CAC, LTV, retention integration) | $90K–$150K | 65% from T1 |
| Tier 3 | Architecture Blueprint (system-level design) | $125K–$250K | 75% from T2 |
| Monitoring | Structural drift detection (12-mo minimum) | $8K–$20K/mo | 75% from T3 |
Tier 1 requires zero internal access — forensic outside-in analysis using public data. Easy enterprise entry point. Each tier validates and extends previous findings with richer data, while depositing structured intelligence into the knowledge graph.
| Tier | Volume | Revenue |
|---|---|---|
| Tier 1 | 6 | $360K |
| Tier 2 | 4 | $480K |
| Tier 3 | 3 | $540K |
| Monitoring | 3 | $540K |
| Total | $1.92M |
Conservative scenario: $1.38M (5/3/2/2 volume split)
Every engagement generates two things:
Over 24–36 months this creates cross-client longitudinal benchmarking, economic linkage models, and industry index authority. The marginal cost of each new engagement decreases as ontology and tooling improve.
| Client | Sector | Outcome |
|---|---|---|
| American Heart Assoc. | Healthcare Nonprofit | Brand power score, viz hub, 5/5 satisfaction — asked for 3 more verticals |
| Fiserv | Fintech (Fortune 500) | Brand diagnosis (46/100) led to $40K naming follow-on engagement |
| FrameBright | Child Safety Tech | Full pipeline, 10 gaps identified, active engagement |
| MicroCo | Entertainment | Weekly intelligence for 22 companies, ongoing SBPI cadence |
| Deckers Brands | Consumer (CPG) | Portfolio gap analysis (HOKA 75, UGG 64), C-suite showcase |
| Long Zhu | Education/Gaming | First layered ontology instance, BMC + value flow analysis |
| AFDVI | Nonprofit | Donation strategy, $100K-$500K revenue opportunity identified |
Every URL below is live. Not mockups, not wireframes. Production intelligence products built by a 4-person team.
Each site was generated from structured knowledge graphs, not manual design. The same pipeline that produces client deliverables also produces the research infrastructure.
This entire portfolio — 7+ engagements, 15+ deployed sites, 76K RDF triples, nightly automation — was produced by four people. Headcount scales linearly. The knowledge graph scales exponentially.
The critical difference is design density. Our 12-class ontology with typed properties, attestation provenance, and SHACL validation means every triple carries structured meaning from day one. A competitor who starts later does not just start behind — they start with an empty database in a domain where historical trajectories ARE the product.
| Phase | Timeline | Key Allocations | Hardware CapEx |
|---|---|---|---|
| Near-Term | 0–6 months | First KG machine, ontology engineer hire, production SPARQL endpoint, API gateway, IP protection + trademarks | $25K |
| Medium-Term | 6–18 months | Machines 2 + 3, ML engineer + platform dev + enterprise sales + client success, managed cloud infrastructure, SOC 2 / GDPR compliance | $50K |
| Long-Term | 18–36 months | GPU cluster, distributed graph infrastructure, full engineering team, multi-region deployment | $350K–$600K |
Infrastructure scales with the knowledge graph, triggered by concrete thresholds:
Total hardware CapEx across all phases: $425K–$675K
The core IP already exists — OWL ontology, SPARQL endpoint, multi-agent pipeline, nightly automation. Investment does not fund R&D. It funds scaling infrastructure, the team to execute the GTM playbook, and the monitoring platform that converts one-time engagements into recurring retainers.
Detailed cost model: Use-of-Funds Spreadsheet
SHUR IQ is a structural brand intelligence platform with a compounding knowledge graph, a self-improving research pipeline, and proven cross-vertical transfer. The IP exists. The clients exist. The traction exists. Investment accelerates distribution across verticals and markets.
Limore Shur — limore@weareshur.com
Nuri Djavit — ndjavit@weareshur.com
Shur Creative Partners — Confidential — March 2026