ShurIQ Investor Demo

01 The AHA Moment Open with this

Talking Points

Shawn Dennis gave AHA's CMO and AI SVP (Charlene Jenner) a live demo using a 2-sentence prompt about "Go Red for Women" and their 100th anniversary
Output appeared in 8 seconds. CMO said it was superior to their $200K agency work
AI SVP (university professor) validated accuracy: "Where did they get this information?"
AHA immediately requested to become a beta/design partner
AHA has a $30M/year marketing budget and runs a CEO Council (Eli Lilly, Kellogg's, top 30 CEOs)

"This is better than the stuff I'm spending all this money for. I don't get this good stuff from my research agency and my marketing agency and the guys I just hired for $200,000 to give me their opinion about what I should do."

AHA Chief Marketing Officer, via Shawn Dennis

AHA

AHA Go Red for Women Intelligence Brief The report that generated AHA's design partner request

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VIZ

AHA Knowledge Graph Visualizations 5-tab interactive viz: gap radar, brand power, discourse flow, composite dashboard

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02 Industry Stack Ranking ~21:38 in call

What to Show

Navigate to Material Movers section first. Show Amazon FataFat as "categorical shift" to pure AVOD
Contrast with competitors: JioHotstar, COL/BeLive using freemium/coin models
Scroll to Gaps: Drama Box remains the only profitable pure play
Show Dimension breakdowns and market intelligence anomalies
21 companies tracked across 6 dimensions. Scores from 82.75 (top) to 21.2 (bottom)

Key Narrative

This is the Tier 1 product: subscribe to an industry view with weekly stack rankings
Stack rankings are proven market manipulators. Companies want to know where they sit
Winners subscribe to stay on top. Mid-tier subscribe to climb. Great for rainmaking

SR

MicroCo Weekly Editorial Live microdrama industry stack ranking with Material Movers, Gaps, Dimensions

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03 Autoresearch Pipeline ~25:15 in call

Talking Points

Running nightly auto-research sessions that generate hundreds of reports testing prediction methods
Prediction accuracy improved 67% since week 1
System identifies gaps and applies them forward: creates per-brand intelligence
Each brand gets a report: "Here's why you changed. Here's where you got hit. Here's where you gained."

23.5% Baseline Accuracy

69.9% Optimized Accuracy

67% Improvement

84 Predictions Evaluated

13 Nights of Automated Intelligence

03Apr

W13 Validation: Mean Reversion Hits 71.4% Highest single-week score. KG optimizer reaches 65.5% via 50-trial random search. iQIYI triple announcement drives +10% stock move.

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02Apr

New Entrants: Google & HolyWater Reshape the Field Google/100 Zeros (63.65) and HolyWater ($22M Series A) enter tracking. DramaBox +4.0 to 82.75. KG config reaches 69.9%.

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01Apr

Structural Gap Analysis: Incumbents vs. Insurgents Netflix, Amazon, Disney share CS weakness (25-40 vs composites 60-76). DramaBox confirmed only profitable pure-play.

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30Mar

Pipeline Recovery & W13 Predictions Locked 84 predictions recorded across 4 methods x 21 companies. Infra resilience tested and passed.

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28Mar

Amazon Downgraded, COL Belive's +47.5pt Hidden Strength Amazon Tier 2 to Tier 3. COL MI score (92) vs composite (44.55) reveals massive structural undervaluation.

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24Mar

Full W12 Backtest: 5 Gainers, 5 Losers Cataloged Complete W12 backtest across 17 companies. Mean reversion at 47.1% is only model beating random.

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22Mar

First Pipeline Run: 9 Directional Signals Emerge Day 1. Platform giants losing ground to vertical specialists. JioHotstar (+5.5) strongest bullish signal.

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NI

Full Nightly Insights Feed All 7 reports with expandable detail: movers tables, dimension anomalies, prediction accuracy, optimizer results

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AR

SBPI Autoresearch Status Report Full accuracy trajectory, optimization results, method comparison

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W13

W13 Prediction Validation Report Week 13 out-of-sample validation results

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04 Client-Level Actionability ~28:40 in call

What to Show

Open the Fiserv report. Explain: "We tested this with my partner Josh, who runs the Stonecastle division that Fiserv acquired"
Show how it combines business intelligence + call analysis into actionable output
Navigate to the priority system: chart/spreadsheet format with owners, status, priority levels
Point out the McKinsey reference: "hiring McKinsey is a signal of problems"
Show how output is structured for direct import into project management systems

"I dug into this and yeah, you nailed it. I don't know how."

Josh Siegel, Stonecastle/Fiserv, on the client-specific report

FV

Fiserv Intelligence Brief Client-specific BI report with gap analysis, priorities, call analysis overlay

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05 Full Traceability ~33:42 in call

Talking Points

Every insight traces back to its origin. Not generative AI. The system composes from building blocks
Show the "Full Quote and Analysis" sections in any report: insight + source + transcript line number
Clients can trace any claim to the atomic level: the original transcript, the original data point
This is the trust mechanism. AHA's AI SVP asked "where did they get this information?" and everything checked out

"It's not creating these insights from thin air. It's composing them from a bunch of building blocks, and it'll tell you what those building blocks are all the way back to the atomic level. You can trace it all the way back to the whole transcript line number seven."

Jonny Dubowsky, during the Shawn call

06 Go-to-Market

Tier 1

Industry Stack Ranking

Weekly report ranking companies by ShurIQ score. Highlights material movers, gaps, and dimension anomalies. Low-cost lead generation and marketing tool.

~$700 /month

Tier 2

Client-Specific Report

Setup fee + ongoing subscription. Client intake defines tracking metrics. Two data models: outside-in (public data) and advanced (client data integration via agentic AI).

Setup + Sub /custom

Vertical Targets

Entertainment (primary, already in it via MicroCo): Microdrama, streaming, media companies
Fintech: Already tested with Fiserv/Stonecastle
Toys & Games: Discussed as third vertical
NGOs: AHA opens the door. Halo effect for brand. NGOs are supported by for-profit pillars
Strategy: Launch 3-4 industry stack rankings to start rainmaking immediately

07 The Appreciating Data Asset

Core Thesis

The knowledge graph compounds weekly with every scoring cycle. 76K+ RDF triples and growing
Expert-designed OWL ontology + SHACL validation + provenance tracking = defensible moat
Insights like "Amazon's distribution moat masks hollow content position" require structured knowledge at scale. No LLM prompt derives this from scratch
Building toward 1B+ nodes per vertical for compound competitive intelligence
The prediction experiments are a test harness for the interface layer. The real products are ontology, SPARQL-queryable structure, dimension anomalies, tier transitions, attestation

SL

SBPI Semantic Layer Explorer RDF/SPARQL architecture, OWL ontology, 12 classes, 22 properties

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KG

Billion-Node KG Thesis Knowledge graph scaling architecture and compound intelligence model

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08 The Raise

$500-600K Seed Round

Eng + Infra Use of Funds

Strategic Investor Profile

Key Points

Use of funds: almost entirely engineering and infrastructure. Hardware costs, engineering sweat. No marketing spend (the tool markets itself)
Seeking strategic investors who provide governance, client introductions, and form the initial board
Not just capital. Investors should help surround the system with the best experts and minds
Small round. Can be family offices, early VCs, or a single partner who wants an exciting thing to do
Revenue strategy: design partners first (AHA model), then scale subscriptions

ID

ShurIQ Investor Deck v2 17-slide pitch deck with revenue architecture, use of funds, traction metrics

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What We Built, What It Does, Why It Matters

Context for These Calls

Talking Points

What to Show

Key Narrative

Talking Points

What to Show

Talking Points

Industry Stack Ranking

Client-Specific Report

Vertical Targets

Core Thesis

Key Points

All Artifacts — Quick Access