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Narrative Hub

RegulateCPG is applied AI infrastructure that enforces regulated operational decisions across jurisdictions.

Confidential Strategy & Technical Verification Hub. All data confidential under NDA.

Section One: Vision & Platform Overview

Platform Overview

Establishing founder-market fit and the inevitability of infrastructure solutions.

READY
Prose | Whitepaper

Infrastructure Thesis

Why regulatory complexity creates a structural moat and Process Authority recognition.

NEEDS PRODUCTION

Workflow Causality

Before vs. After RegulateCPG

Traditional SME workflows are bottlenecked by fragmented judgment plus administrative overhead. Institutional knowledge loss is structurally prevented by collapsing processes into judgment-only execution.

~80% Process Acceleration
Traditional
RegulateCPG

Section Two: Product Demonstration Suite

Demo | 1m

Adding an Ingredient

AI ingredient classification, storage/certification extraction, and nutritional values auto-ingest from TDS docs.

READY
Demo | 1.5m

Building a Recipe

Advanced composition, scaling, financial analysis, fact panels, and FDA/eCFR compliance verification.

READY
Demo | 1m

NFP Output & Accuracy

Real-time Nutrition Facts preview, dynamic formatting, and AI-driven eCFR + rounding compliance checks.

READY
Reports | Intelligence

Insight Suite: Consumer-Driven Recipe Development

Extract brand-specific consumer feedback, track live processing, and convert insights into optimized formulations.

READY

Section Three: Intelligence & Governance

Architecture

RAG Search Knowledge Base

Upload company documents, query in natural language, and generate cited reports with AI-powered retrieval.

READY

Section Four: Deal Pipeline & Traction

Interactive Chart

Pipeline Visualization

Repeatability over opportunism. Channels (EcoLab) and Ecosystem exposure.

READY / NEEDS COLL.
Calculator

EcoLab Unit Economics

Scaling without a sales army. Channel leverage modeling.

NEEDS COLLECTION

Section Five: Financial Architecture

Model | PDF

Business Plan & Projections

Three revenue layers: Historical, Current ARR, and Pipeline-weighted.

READY
Industry Cards

Scalability Adjacencies

Expansion as architectural reuse in Life Sciences, Pharma, and beyond.

NEEDS PRODUCTION

Section Six: Strategic Anchors

Case Study

Campbell's Scaling

Structural embedding in a $10B+ product portfolio. Economic proof.

READY
Founder Core

The Founders

Restraint and execution focus. Strategy & Architecture.

READY

Platform Overview

Anchor Asset: Founder Narrative & Regulatory Authority

RegulateCPG Platform Overview

RegulateCPG: The intelligent operating system for modern CPG brands.

RegulateCPG is an end-to-end, AI-driven Product Lifecycle Management (PLM) and compliance platform that bridges R&D, commercial strategy, and federal compliance. It transforms manual, siloed workflows into a single high-velocity system of record.

The Power of AI-Driven Compliance

Live eCFR & FDA Sync

Continuously verifies data against current federal standards.

Zero-Touch NFP Generation

Auto-extracts nutrition values and generates export-ready labels.

Automated Verification

Validates PDCAAS, DV logic, and formula claims for scientific/legal soundness.

Key Modules & Features

Module Core Functionality
Ingredient & Recipe Mgmt Centralized library with allergen tracking and multi-level categorization.
Nutritional & Supplement Facts One-click generation for food and supplement formats with built-in rule logic.
Insight Suite Scans market trends and sentiment for de-risked product ideation.
RAG Search Natural language queries across proprietary documents and recipes.
Costing & Yield Real-time cost cards with moisture loss, yields, and overages.
Sensory Analysis Feedback loops that align lab iterations with consumer gold standards.

Why RegulateCPG

80%
Faster to market by automating compliance busywork.
96%
Labeling accuracy to reduce recall risk and manual errors.
Always
Audit-ready with complete timestamped formula history.

RegulateCPG is not just a compliance tool. It is a growth engine that gives R&D more time to innovate and less time filling out forms.

Core Narrative Beats

  • The fragmentation of compliance infrastructure.
  • The indifferent regulator (The "SME Gap").
  • The accelerating loss of institutional knowledge.
  • Why RegulateCPG is not generic "AI in a Box".

The Infrastructure Thesis

RegulateCPG is applied AI infrastructure that enforces regulated operational decisions across jurisdictions.

Regulatory complexity is not a hurdle to be cleared; it is a structural moat to be fortified. In the $4.5T global food industry, the trust layer has historically been human-dependent. RegulateCPG transitions that trust into an algorithmic infrastructure.

Pacific Foods Case Study

Deployed our vertical infrastructure to reduce compliance huddle cycles by 84%. Result: 28 months faster time-to-market on new formulations. Asset Value: $13M NPV improvement.

Why This Cannot Be Solved with Headcount

The "Silver Tsunami" of SME retirement is not a theoretical risk; it is a live operational failure. Manufacturing organizations are currently leaking institutional knowledge faster than they can replicate it via academic pipelines. RegulateCPG captures the "Shadow Records" of these operators and turns them into governed, searchable intelligence.

Institutional knowledge loss is structurally prevented by collapsing fragmented processes into judgment-only execution.

Capital Efficiency Proxy $66.1M Lower-bound annual savings for Ventura ROI anchor

Adding a New Ingredient

Establishing a governed record from TDS and nutritional documents using built-in AI tools for classification, compliance metadata, and nutrition extraction.

Demo Ingredient: Chocolate Chips | AI parses TDS/Nutrition docs into governed fields

System Output: AI Ingredient Ingestion

Ingredient Name Chocolate Chips
Ingredient Classification Inclusion / Chocolate
Storage Requirements Cool, dry (15-22 C)
Certification Non-GMO, RSPO MB
Religious Authority Kosher Dairy Certified
Expiry Date 12 months from production
Nutrition Values Source Auto-extracted from TDS/Nutrition Doc

Building a Recipe

Our Recipe Module streamlines formulation from composition to final compliance, with built-in verification for net weight, serving size, and units before market release.

End-to-end recipe build: composition, scaling, cost, facts, PDCAAS, and compliance checks.

Recipe Module Capabilities

  • Nested recipes for complex product composition
  • Yield calculation with processing loss/gain adjustments
  • Verification checks for net weight, serving size, and units
  • Working BOM population with formulation scaling
  • Live compliance status updates while editing

Regulatory Compliance Verification

FDA Check PASS
eCFR Alignment SYNCED
Net Weight / Units VERIFIED
Serving Size Logic VALID
Cross-reference automation keeps ingredient and label claims aligned with active FDA + eCFR rulesets.

Financial & Nutritional Analysis

Module Output State
Cost Cards COGS + Margin Breakdown READY
Fact Panels Nutrition + Supplement Facts 1-CLICK
PDCAAS Protein Quality Score CALCULATED
Yield Engine Final Weight/Volume LOSS/GAIN ADJUSTED

NFP Output & Accuracy

Dynamic, real-time Nutrition Facts Panel preview that updates as formulations change, so labels stay brand-ready and regulation-ready in the same workflow.

Short NFP walkthrough: preview updates, formatting variants, and compliance checks.
Dynamic NFP Customization
Nutrient Control
Vitamin D ON Potassium OFF Calcium ON
Target %DV Profile
Infants Toddlers Adults
Flexible Formatting
Standard NFP Dual-Column NFP Simplified NFP
Live NFP Preview
Live Nutrition Facts Preview

Instantly updates with formula and format changes

Comprehensive Data Export

  • Nutrition Summary export for internal nutrient review
  • Formulation Specs export for R&D to production handoff

AI-Driven Compliance & Precision

Feature Function
eCFR Alignment Scans labels against active FDA/eCFR guidance and flags non-compliance.
Smart Rounding Applies required rounding logic, including calorie rounding to nearest 5/10.

RAG Model: Governance in Action

Structure vs. Ambiguity: "The system cannot produce a non-compliant output by design."

RAG Search walkthrough: retrieval, policy grounding, and jurisdictional response.

Multi-Format Data Ingestion

  • Bulk upload support for files and entire folder structures
  • Accepted formats: PDF, DOC/DOCX, TXT, CSV

AI-Powered Indexing

  • Automatic parsing and chunking with embedding models
  • Secure indexed storage for high-speed retrieval

Natural Language Querying

"What are our shelf-life protocols for dairy products?"

Automated Report Generation

Synthesize information across multiple uploaded documents into concise, meaningful reports for operations and compliance teams.

Source Attribution & Auditability

Answer Segment Citation Source
Shelf-life protocol summary `Dairy_QA_Manual.pdf` p.14
Temperature exception rule `Cold_Chain_SOP.docx` p.3
Validation reference `ShelfLife_Limits.csv` row 27

Pipeline Visualization

Showcasing Repeatability & Channel Driven Scale

EcoLab Channel $125M Potential
2,400 Shared Sites LOI / Deployment Phase
Direct Enterprise (Ventura, Maple Leaf) $8.2M ARR
8 Core Clients Strategic Expansion

Financial Projections

Revenue Bridge: Historical, Platform, and Channel Scaling

Revenue Architecture Audit Ready
Recognized Revenue (Agency+Platform)
$1.8M
LTM Actuals
Exit Run Rate (Dec 2025)
$2.4M
Based on normalizing contracts
Projected 2026 Revenue
$9.0M - $10.5M
Ecolab Channel Contribution
Critical Milestone
October 8, 2025

Official launch of external subscriptions. Revenue bend observed within 72 hours of pricing deployment.

"When we turned pricing on, revenue followed immediately."
The Ask (Seed Execution)
$3.0M SAFE
20% Discount | $20M Cap
Execution Intensity Phase
Use of Funds
  • Deployment Capacity Scaling
  • Channel Execution (EcoLab)
  • Platform Hardening
P&L Notes
~$400K Net Loss

Founder salaries included at market rates. Growth reinvestment focus.

Projected 2026 revenue bridge chart

Path Forward: Series A success intent

1
$10M+ Enterprise ARR

Embedded in 3 key verticals via architectural reuse.

2
Standardized Deployment

Throughput governed by system, not headcount.

3
Proven Execution

Demonstrated capitalization on channel leverage.

Bottleneck Statement

"The limiting factor is deployment throughput."

Standardized scaling via channel leverage prevents sales army friction.

EcoLab Unit Economics Calculator

Channel leverage vs. Linear Sales Headcount

Base Economic Intuition
$5,000 / site license
2,500
$50,000
SCENARIO: BASE GROWTH

"Scaling without a sales army — Channel harness leverage."

Projected Channel ARR $125.0M

Direct Sales Comparison: -70% OpEx Harness

Insight Suite: Consumer-Driven Recipe Development

Use brand-specific consumer feedback to move from raw sentiment to a market-aligned formulation with measurable confidence.

Getting Started Workflow

  • Initialize: Open Insight Suite from the Tool section and select Add Recipe.
  • Categorize: Set Category and Sub-Categories (comma-separated) for precise benchmarking.
  • Extract: Select target brand and product, then run the Data Extraction engine.

Live Processing Status

Step Status Output
Brand Feedback Ingestion Processing Sentiment corpus building
Category Benchmarking Processing Peer baseline comparison
Pain Point Cluster Detection Completed Dashboard insights unlocked

Data Processing & Analysis

When status transitions from Processing to Completed, the module unlocks a dashboard of graphs and visualizations for:

  • Consumer pain point identification
  • Preference signal ranking by category
  • Brand-to-brand performance benchmarking
  • Recipe optimization direction for next formulation
Insight Suite walkthrough: extraction, live processing, and dashboard unlock.
Chosen Brands Mayonnaise word cloud
Word frequency heatmap across flavour

Consumer feedback is transformed into formulation actions inside the same workflow.

Scalability: Adjacent Industries

Architectural reuse over market sprawl. Mapping regulatory overlap.

Life Sciences

88% Overlap

FDA 21 CFR Part 11 consistency. Validation acceleration for clinical formulations.

Entry: H1 2027

Cosmetics & OTC

94% Overlap

MoCRA alignment. Ingredient safety substantiation and automated labeling.

Entry: H2 2026

Marketing Performance

Subscribers
43.4K
Open Rate
46.4%
MQL to SQL
11.2%
Deal Velocity
+22%
Asset: Growth Trend & Conversion Waterfall Visualization

Strategic Proof

Case Study: Campbell's Enterprise Scaling

1. Scope & Execution

Deployment across 4 core product families (Soup, Snacks, Beverage, Meals). Unified 14 disparate formulation databases into a single governed algorithmic infrastructure.

2. Lifecycle Coverage

Direct enforcement of regulatory constraints from raw ingredient procurement through final Nutrition Facts Panel (NFP) generation.

Economic Impact
$4.2M
Annualized OpEx Savings (Phase 1)
Time-to-Market Acceleration +62%
SME Handoff Cycle Compression 22 -> 1

Vulnerability Mitigation

"The pressure test. Campbell's represents the most complex regulatory environment in the CPG sector. Our system enforced compliance across 40+ global jurisdictions during the 2024 reform cycle without a single SME manual override."

Execution Foundation: The Founders

Mark Haas, CEO

Mark Haas, CEO

CPG industry veteran with 30+ years of executive leadership at brands including Kellogg's and Annie's. Mark currently serves as CEO of Regulate and The Helmsman Group.
Proven entrepreneur and strategic advisor, recognized across major media outlets and industry boards for product commercialization expertise.
Ishan M. Subedi, Ph.D., CTO

Ishan M. Subedi, Ph.D., CTO

Visionary serial entrepreneur and technologist with 10+ years building scalable enterprise solutions across healthcare, AI, and CPG sectors.
As CEO of Dhuni Software, he bridges cutting-edge NLP research with practical business applications, driving transformational growth for startups and Fortune 500 companies.

Investment Conclusion

Ready for Seed Execution

We have structural embedding, a validated channel wedge, and a proven enforcement engine. This round funds deployment throughput.

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