Digital Galactica Labs

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Building Research-Grade Infrastructure for Personalized Nutrition 

Founded in 2022, DGL  develops deterministic nutrition intelligence systems designed to translate population-level dietary guidance into individual, context-aware recommendations—without black-box algorithms.

We build the infrastructure layer that makes personalized nutrition transparent, reproducible, and economically accessible.

Current nutrition technology fails in three critical ways:

Single-number scoring collapses complex physiology into oversimplified ratings that can't serve individual health contexts.

Black-box AI provides recommendations without explaining why, making clinical validation and regulatory compliance impossible.

Economic blindness optimizes for health without considering budget constraints, widening disparities for those who need guidance most.

We're building a different approach.

The DNI (Digital Nutrition Intelligence) Ecosystem is a suite of nine integrated, patented systems spanning the complete nutrition intelligence pipeline:

Data Foundation (UNL): Unified nutrition data standardization across 500,000+ foods from USDA FoodData Central and commercial databases.

Deterministic Scoring (DNE): Five-dimensional nutrition evaluation measuring glycemic impact, nutrient density, energy density, processing level, and overall health—using explicit mathematical formulas, not machine learning.

Personalization (FFHS): Focus-Fit Health Scoring enabling users to weight six physiological dimensions (blood sugar, cardiovascular, digestive, inflammation, performance, wellness) based on individual health contexts.

Economic Optimization (HPD): Health-Per-Dollar scoring that optimizes nutrition within budget constraints, making healthy eating economically accessible.

Meal Planning (AMPE): Constraint-based meal optimization balancing health goals, preferences, cost, and food waste reduction.

Shopping Intelligence (ISLG): Multi-store optimization generating budget-aware shopping lists from meal plans.

Spoilage Prediction (SPM): Bayesian models reducing food waste through freshness tracking.

Performance Monitoring: Real-time system validation ensuring 100% deterministic reproducibility.

All systems use predetermined mathematical formulas. No training data. No model drift. No black boxes.

Same inputs produce identical outputs—every time, for every user, forever.

This isn't just good engineering. It's a prerequisite for clinical validation, regulatory compliance, and research reproducibility.

Most nutrition apps use machine learning trained on user behavior. This creates non-reproducible recommendations that can't be validated in clinical trials, audited for compliance, or explained to users.

We chose a different path: deterministic algorithms with full transparency.

Every score traces back to specific nutrient values and evidence-based formulas. Clinicians can audit the reasoning. Researchers can replicate findings across populations. Users can understand why foods scored differently for their context.

When dietary guidelines change, we adjust interpretation layers—not the underlying measurement systems. When new evidence emerges, we update formulas transparently, not retrain opaque models.

Determinism enables what black-box AI cannot: trust, validation, and regulatory pathways for clinical use.

The DNI ecosystem has been tested with 10,000+ synthetic user profiles representing diverse health conditions, activity levels, dietary restrictions, and budget constraints:

• 100% deterministic consistency (0% variance across identical inputs)
• 87.5% health goal achievement across diverse populations
• 69.5% food waste reduction through spoilage-aware optimization
• 14.3% cost savings while maintaining nutritional adequacy
• 7.8-week average convergence to stable user preferences
• <150ms processing latency per recipe evaluation

Critical caveat: These metrics come from computational testing, not clinical trials. We have not validated that dimension-optimized guidance improves biomarkers or health outcomes in real populations.

That's why we're seeking research partners.

We are not providing medical nutrition therapy. We are not replacing registered dietitians or physicians.

We are building research-grade infrastructure that enables:

• Academic researchers to study personalized nutrition at scale
• Healthcare systems to implement evidence-based dietary guidance
• Policy makers to understand economic barriers to nutrition access
• App developers to integrate transparent, auditable nutrition intelligence

We provide the tools. Others provide the expertise, clinical judgment, and patient relationships that make nutrition interventions effective.

We're actively seeking academic and clinical research partnerships in:

Biomarker Validation: Do dimension-specific scores correlate with HbA1c, lipid profiles, inflammatory markers, and other health outcomes?

Implementation Science: What combination of technology and professional support optimizes adherence and outcomes?

Health Equity: Can budget-optimized nutrition scoring reduce diet quality disparities without increasing food expenditure?

Population Generalizability: Do deterministic formulas work across diverse age groups, ethnicities, and clinical populations?

We have operational infrastructure. We have provisional patent protection. We have commercial interests.

We also have genuine uncertainty about what works—and we believe that uncertainty should be resolved through rigorous research, not assumptions.

We have filed nine provisional patent applications covering the complete DNI ecosystem:

1. DNI Foundation (Base Architecture)
2. DNE (Deterministic Nutrition Engine)
3. FFHS (Focus-Fit Health Scoring)
4. HPD (Health-Per-Dollar Optimization)
5. AMPE (Adaptive Meal Planning Engine)
6. ISLG (Intelligent Shopping List Generator)
7. UNL (Unified Nutrition Ledger)
8. SPM (Spoilage Prediction Model)
9. DNI Ecosystem (Master Integration)

Our IP strategy protects the deterministic approach, multi-dimensional framework, and economic optimization—not individual formulas. We want others to build on this work, not be blocked by it.

Taylor Reasoner, MPA
Founder & Chief Executive Officer

System architect and researcher building deterministic nutrition intelligence infrastructure. 30 years public sector emergency medical services. Bachelor's and Master's in Public Administration, pursuing DrPH. Founder of Gatehouse Asset Management LLC. Not a clinician or registered dietitian.

Steve Harkness
President & Chief Operating Officer

Three decades public sector operational leadership in California emergency management and Incident Command System (ICS) coordination. Specializes in large-scale, multi-jurisdictional operations, regulatory compliance, and mission-critical systems deployment. Applies public sector operational discipline to research-grade technology infrastructure.

Our Approach: Academic-adjacent rigor without academic bureaucracy. Commercial sustainability without compromising research integrity. Open to collaboration, transparent about limitations.