SHUR IQ — Executive Summary — Week of March 24, 2026
Executive Summary — March 2026

A Billion-Item Database That Gets Smarter Every Week

SHUR IQ now has a self-improving research pipeline, a formal ontology encoding competitive intelligence as structured IP, and cross-vertical proof that the system transfers to any industry in days.

Prepared for internal review — Shur Creative Partners

The breakthrough this week

We built an autoresearch pipeline that optimizes its own prediction accuracy nightly. Directional accuracy jumped from 47% to 70% in the first run. The system now generates, tests, and refines hypotheses about competitive dynamics without human intervention. Every report we produce, every graph we build, every insight we validate feeds a growing knowledge base that makes the next analysis sharper.

69.9%
Directional Accuracy
+46%
vs. Prior Method
2
Verticals Live
75K+
RDF Triples (IP)

What Changed This Week

Before this week, SHUR IQ produced intelligence reports. Good ones. After this week, SHUR IQ produces intelligence reports and accumulates structured IP with every engagement. The difference matters for investors.

Three things happened

1. The knowledge base became formal. Every company we score, every dimension we evaluate, every signal we track is now encoded as RDF triples in an OWL ontology. Not a spreadsheet. Not a document. A queryable, validated, machine-readable knowledge graph with 75,000+ triples across two verticals. This is the database.

2. The research pipeline became self-improving. A nightly optimization cycle (Markovick et al. 2025 methodology) tunes 12 parameters that control how the knowledge graph translates into predictions. First run: 69.9% directional accuracy, up from a 47.1% baseline. Five more experiments are queued, each compounding on the last.

3. Cross-vertical transfer was proven. The same ontology, the same pipeline, the same scoring framework produced intelligence for micro-drama entertainment (22 companies) and AI agent infrastructure (1,672 companies). Onboarding cost for a new vertical: zero dollars, 2-3 days of analyst time.

The Intellectual Property

SHUR IQ's IP is not code. Code can be replicated. The IP is the encoded intelligence — the ontology that defines how competitive dynamics work, the knowledge graphs that accumulate company-specific structural data week over week, and the optimization parameters that convert that data into predictions.

What the ontology encodes

The SBPI (Structural Brand Power Index) ontology defines 12 core classes, 22 properties, and 5 scoring dimensions. It models companies, verticals, weekly score records, market signals, attestation provenance, and predictions as formal linked data.

DimensionWeightWhat It Measures
Distribution Power25%Platform reach, audience scale, algorithmic discovery
Content Strength20%Volume, quality, exclusivity, IP pipeline
Narrative Ownership20%Press control, thought leadership, brand recognition
Community Strength20%Engagement, fan ecosystems, creator partnerships
Monetization Infrastructure15%Revenue model maturity, payment systems, subscription economics

What accumulates

Every week, every vertical adds ~200-400 new triples. Company scores, dimension breakdowns, market signals, attestation records, predictions, and their outcomes. After 12 weeks in micro-drama alone: 2,588 triples. The AI agent vertical added 73,559 in one pass. This is a compounding asset.

The database, concretely

VerticalCompaniesWeeksRDF TriplesTop Score
Micro-Drama22122,588ReelShort 88.0
AI Agent Infrastructure1,672173,559Persana AI 69.0

Each new vertical reuses the ontology. Each week deepens the intelligence. The marginal cost of adding a vertical approaches zero.

Why this is defensible

Competitors can build dashboards. They can run LLM queries. What they cannot replicate is 12 weeks of validated, provenance-tracked competitive intelligence encoded as formal linked data with SHACL validation and SPARQL queryability. The knowledge graph is the moat. It compounds.

The S&P analogy

S&P built a business worth $140B by scoring companies on creditworthiness. SHUR IQ scores companies on structural competitive advantage. The methodology is transparent. The data accumulates. The index becomes the reference. The difference: we can stand up a new vertical in days, not years.

The Autoresearch Pipeline

Built this week. Running nightly. Five experiments queued. The pipeline takes academic methodology (Markovick et al. 2025, "Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning") and applies it to SHUR IQ's domain-specific knowledge graphs.

How it works

Weekly Data → RDF ETL → SPARQL Queries → Prediction → Optimization → Validation

The 9-phase weekly cycle runs three parallel research tracks:

TrackFunctionOutput
A — Event ImpactDiscovers external events, maps them across 5 SBPI dimensions, classifies as MATERIAL / MONITORING / NOISEImpact reports stored as RDF
B — Defensive BIGenerates mitigation strategies for MATERIAL events only. Dual-frame scoring (tactical + strategic) prevents reactive noiseActionable recommendations
C — Signal OptimizationTPE hyperparameter tuning learns which signal weights maximize human-validated accuracyOptimized config (12 parameters)

Experiment 2 results (first run, this week)

MethodAccuracyvs. Baseline
Persistence (naive)23.5%—
Momentum (naive)23.5%—
Mean reversion47.1%+100%
KG-augmented (unoptimized)23.5%—
KG-optimized (Exp 2)69.9%+197%

30 TPE trials, 51 company-week observations, 12 optimized parameters. Running nightly at 6:13 AM.

What's queued

Experiment 1 — Goodhart Guard (safety net, ready now)

Current 30-trial/51-observation ratio (0.59) exceeds safe optimization regimes. The guard detects overtuning before it degrades production accuracy. Prevents 2-5 percentage point regressions. Zero additional data required.

Experiment 3 — Dimension Weight Optimization (needs 6+ weeks data)

Current dimension weights (25/20/20/20/15) are domain intuition. Learned weights via TPE expected to improve accuracy 5-15%. Covariate-dependent weights (different optimal weights per industry segment) add another 3-8%.

Experiment 4 — Temporal Decay Signals (needs 8+ weeks data)

Add exponential decay weighting to historical lookback. Current system treats all weeks equally. Gastinger et al. (2024) shows temporal decay ranks 1st-3rd across temporal knowledge graph benchmarks. Expected 8-15% accuracy improvement.

Experiment 5 — Cross-Vertical Transfer (after Exp 3-4 stable)

Warm-start AI agent vertical (1,672 companies) from optimized micro-drama config. Zeng et al. (2025) shows 21% one-shot improvement. Expected 40-60% reduction in trials-to-convergence. Validates the entire "one ontology, many verticals" thesis.

Why the sequence matters

Each experiment compounds on the last. Guard first (safety), then multi-objective calibration (foundation), then dimension weights (expand search space), then temporal signals (deeper history), then cross-vertical transfer (prove generalizability). Skip a step and downstream results are unreliable.

How Value Compounds

Most competitive intelligence is a commodity. Research it, deliver it, start over. SHUR IQ breaks that pattern because every engagement feeds a permanent knowledge base, and the knowledge base makes the next engagement faster and sharper.

Three compounding loops

Loop 1: Knowledge Transfer

When we score a company in micro-drama, we learn about distribution dynamics, content economics, and community engagement patterns. Those patterns transfer. A future client in podcasting or gaming content will benefit from structural insights encoded during micro-drama analysis. The ontology is vertical-specific in its data but universal in its framework.

Loop 2: Prediction Accuracy

Every week of data makes the optimizer smarter. Week 3: 51 observations, 69.9% accuracy. Week 12 (projected): 200+ observations, significantly tighter confidence intervals. The nightly optimization cycle re-tunes against all available history. Accuracy improves automatically as data accumulates. No additional engineering required.

Loop 3: Engagement Revenue + IP

Every $100K+ engagement produces revenue and deepens the knowledge graph. The graph is the IP. Dual-revenue compounding: cash flow from services, knowledge graph appreciation from the work itself. The competitor who starts later starts with an empty database.

The cost structure

ActivityFirst VerticalEach Additional
Ontology designBuilt (sunk cost)$0 — reuse
ETL pipelineBuilt (sunk cost)$0 — reuse
SPARQL query library11 queries built$0 — reuse
Optimization pipelineBuilt (sunk cost)$0 — warm-start transfer
Vertical onboarding2-3 weeks2-3 days
Weekly operationAutomated (nightly)Automated (nightly)

What investors get

A platform business disguised as an agency. Agency economics on the front end (high-touch client engagements at $40K-$250K). Platform economics on the back end (knowledge graph appreciates, prediction accuracy compounds, marginal cost of each new vertical approaches zero).

The expert knowledge graph thesis

General-purpose knowledge graphs (like ATLAS) need a billion facts to be useful. Expert-designed, domain-specific knowledge graphs hit their useful threshold at 10K-100K facts. We're already past that threshold in two verticals. The question isn't whether the system works. It's how many verticals we stand up in year one.

Delivered This Sprint

Everything below was built since the last team meeting. Each link is a live, deployed site demonstrating a different facet of the SHUR IQ system.

Experiment Lab
KG-LLM Interface Optimization report. Experiment 1 baselines, Experiment 2 results (69.9% accuracy), methodology documentation, 30-trial scatter plot.
SBPI Semantic Layer
The system architecture. OWL ontology, ETL pipeline, SPARQL query library, live results, and investment thesis. The technical foundation.
Cross-Vertical Dashboard
Side-by-side comparison: micro-drama (22 companies, 12 weeks) vs. AI agents (1,672 companies). Same ontology, same pipeline, different verticals.
SHUR IQ Style Guide
Prompt engineering reference for report generation. 17 numbered rules, header system, readability constraints. How we enforce quality at scale.
SHUR IQ Explainer
Product overview. Interactive Attention Stack demo, customizable ontology walkthrough, engine-vs-ontology architecture separation.
Micro-Drama Weekly (Issue 3)
Latest weekly competitive intelligence report. 22 companies, tier rankings, breaking news, SBPI deltas. The production output.

Infrastructure built (not visible as sites)

ComponentStatusWhat It Does
Nightly optimization cycleLive9-phase pipeline runs at 6:13 AM daily. Loads data, validates predictions, re-optimizes parameters.
3-track BI agent systemLiveEvent impact (A), defensive recommendations (B), signal weight optimization (C) running in parallel.
OWL ontology + SHACL validationLive12 classes, 22 properties. Formal schema with shape validation on every ETL run.
SPARQL query library11 queriesWeekly movers, tier transitions, anomaly detection, prediction accuracy, cross-vertical comparison.
Cross-vertical ETLProvenSame pipeline produced intelligence for micro-drama and AI agents. Zero code changes between verticals.

Week-over-week progress

MetricLast MeetingNowChange
Prediction accuracy47.1% (mean reversion baseline)69.9% (KG-optimized)+22.8 pts
RDF triples (total)~2,500 (micro-drama only)76,147 (2 verticals)+29x
Verticals operational12+1
Automated research tracks03 (A/B/C)+3
Queued experiments05 (paper-grounded)+5
Nightly optimizationManualAutomatedAutomated