Strategic Transformation // Verified

Personalized Nutrition: 60% Faster
Meal Planning.

Engineering a microservices-based AI ecosystem for Eating All Together LLC, utilizing Computer Vision and Geolocation to bridge the gap between clinical nutritional goals and daily lifestyle execution.

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Outcome_TelemetryBIO_INTEGRITY_VERIFIED
60%
Planning Efficiency
↑ AI_AUTOMATED
50k+
Product Queries
Sub-Second Latency
High
DAU Retention
FRIDGE_SCANNER

Trusted by Leading Fortune 500 Innovators

/// Strategic Context

The Mission: Ecosystem Nutrition.

Vertical
Digital Health / B2C

Holistic nutrition platform specifically architected for women’s metabolic and lifestyle requirements.

Engagement
Strategic Pod

Full-stack mobile engineers + AI/CV Specialists + Cloud Architects embedded to build a scalable microservices backend.

Objective
Functional Autonomy

Simplifying complex calorie tracking and grocery logistics into an autonomous, AI-curated user journey.

Technology
AI-Driven Mobile Mesh

React Native, OpenCV (Computer Vision), Geolocation APIs, and Node.js Microservices architecture.

/// The Situation

The Reality Gap: Fragmented Tracking.

The client identified a systemic failure in existing digital health tools: users were forced to toggle between disjointed calorie trackers, recipe blogs, and grocery apps, leading to high abandonment and tracking fatigue.

The technical challenge was structural: creating a nutrition ecosystem that was culturally adaptable and holistic—integrating real-world grocery data and local restaurant nutritional values without the lag typical of monolithic mobile apps.

User Attrition
Fragmented user journeys created high friction, causing users to drop off after 7 days of manual logging.
Data Latency
Standard product database lookups were exceeding 2.5s, unacceptable for a high-traffic B2C mobile environment.
Rigid Architecture
Legacy monolithic structures prevented the rapid rollout of modular features like localized restaurant finders.
/// Architecture

The Operational Gates

01
Decoupled Microservices
Architected the backend into independent services for meal logic, grocery automation, and user profiles to ensure 99.9% availability.
Infrastructure
BackendNode_Microservices
SyncREST_Async
DeploymentModular
02
Vision-Enabled Recognition
Implemented a 'Fridge Scanner' using OpenCV to intelligently identify ingredients and map them to recipe nodes in the graph database.
AI_Vision_Layer
ToolkitOpenCV_OCR
ModelImage_Classification
EngineFridge_Scanner
03
Geo-Nutritional Integration
Fused Geolocation APIs with nutritional metadata to build a real-time 'Healthy Restaurant Finder' for travelers.
Geolocation_Mesh
APIGoogle_Maps_Nutrition
FilteringDynamic_Macro
StatusLIVE_FEED
/// The Architecture Shift

The Structural Evolution.

Dimension
Traditional App
AI Nutrition Mesh
Meal Planning

Manual Selection

Users spent 45+ mins/week manually selecting recipes and calculating macros.

AI Synthesis

Autonomous generation of weekly plans reduced user time by 60%.

Scalability

Monolithic Lag

Product searches slowed down linearly as the user base and database expanded.

Elastic Queries

Modular architecture maintained sub-second latency across 50k+ product queries.

Grocery Sync

Static Lists

Manual entry required for grocery lists with no link to meal plan inventory.

Auto-Orchestration

Bi-directional sync between planning and grocery automation via event-driven triggers.

/// The Secret Sauce

Implementation Highlights.

AGENTIC_AI

Intelligent Fridge Scanner

Computer Vision layer that identifies inventory and suggests recipes, solving the 'empty fridge' conversion blocker.

Impact // Retention
Industry-Leading DAU
LOW_LATENCY

Sub-Second Product Index

Optimized database indexing and caching for over 50,000 nutritional products, ensuring instantaneous UI response.

Impact // Performance
Sub-1s Search
UX_DEPTH

Nutritional Restaurant Finder

Filters local dining options by specific dietary requirements, extending the platform's utility outside the home.

Impact // Ecosystem
60% Time Saved
/// Proprietary Assets

Accelerated by Coretus Kernels™.

Health Identity Kernel

Pre-built HIPAA-compliant auth modules for securing sensitive biometric and dietary data.

Bio-Telemetry Mesh

Real-time monitoring for user engagement spikes and database query health.

Geo-Integration Kernel

Reusable templates for fusing maps and custom nutritional data points at scale.

Vision-Ingestion Kernel

Optimized OpenCV wrappers for mobile-native image recognition and ingredient classification.

Time_To_Production
40% Faster
Standard Build22 Weeks
Coretus Accelerated13 Weeks
By deploying our Vision-Ingestion and Geo-Integration Kernels, we bypassed 9 weeks of R&D, focusing 100% on the core AI recommendation logic.
/// Verification

The Performance Delta.

METRIC: EFFICIENCY

User Time-to-Plan

AI-automated grocery and meal planning eliminated the cognitive load of manual tracking.

Manual LogHigh Friction
EAT Platform60% Faster
↓ 60% Planning Time
METRIC: SCALE

Query Throughput

Modular architecture allowed the platform to handle massive concurrent product lookups with zero degradation.

MonolithCongested
Microservices50k+ Queries
Sub-Second Latency
METRIC: CONTINUITY

Uptime Protection

Service decoupling ensured that a spike in geolocation requests did not affect the core recommendation engine.

Target99.9%
ActualVerified
99.9% Service Uptime
Outcome Status: Performance & HIPAA Compliance Verified
/// Governance

Operational Integrity.

01
Data Privacy
Architected with HIPAA-gated data meshes and PII scrubbing to protect sensitive health information.
Status: HIPAA_READY
02
Compliance Standards
SOC2-aligned cloud infrastructure with full audit-trails for all data access and modifications.
Status: SOC2_READY
03
Sovereign Ownership
Coretus provided 100% IP transfer of the mobile codebase, microservices, and AI recognition logic.
Status: 100% OWNED
04
Deployment Agility
K8s-orchestrated environment allowing for zero-downtime rolling updates of meal-planning modules.
Status: K8S_OPTIMIZED
Discuss HealthTech ROI
“
Coretus didn't just build an app; they engineered a personalized nutrition ecosystem that reconciled clinical dietary needs with our users' fast-paced realities. Our planning time dropped by 60% immediately.

Ashley Nowak

CEO // Eating All Together LLC

/// Vertical Depth

Synergistic Outcome Mesh.

Art Market Intelligence
90%
Research Efficiency

Art Investment: 100% Fully Automated Intelligence.

The Reality Gap

Human fatigue led to missed undervalued lots during high-volume auction cycles on platforms like Drouot.

The Outcome

Architected independent microservices for secure OAuth2 authentication and anti-bot resilient scraping to extract structured artwork metadata.

INTELLIGENCE_VERIFIED
Explore
Digital Health & Wellness
60%
Time Reduction

Nutrition Planning: 60% Faster AI Automation.

The Reality Gap

Manual meal logging took users an average of 45 minutes daily, resulting in 70% drop-off within the first 14 days.

The Outcome

Engineered functional layers (recommendations, geolocation, auth) as independent services to ensure horizontal scalability.

ECOLOGY_READY
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Global HRTech
45%
Variability Drop

Talent Audits: 45% Less Interview Variability.

The Reality Gap

Unconscious bias in subjective interviews led to skewed diversity metrics and missed high-potential talent.

The Outcome

Developed a custom NLP pipeline to extract structured competency signals from interview transcripts, neutralizing linguistic styles.

EQUITY_VERIFIED
Explore

Turn Nutrition into a Strategic Asset.

Replace fragmented food logging with auditable AI ecosystems. We engineer zero-drift health pipelines for high-retention engagement—securing your HIPAA compliance while eliminating user friction.

HIPAA & SOC2 Aligned

Sub-Second Search Latency

100% IP & Model Ownership

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