ELITE-PULSE / BIOMETRIC AI

ELITE-PULSE

A biometric decision-intelligence platform for pressure moments, connecting a mobile app, GraphQL facade, AI Mentor, biometric signals, and internal ops.

System design Security AI/ML

Client: ELITE-PULSE Year: 2026

Biometric privacy by architecture

Where the project started

ELITE-PULSE separates a user's facts from AI interpretation: the Mentor works on encrypted, versioned profile projections, never on raw tables, and never overwrites historical facts.

Starting point

Wellbeing apps, productivity tools, health data, and AI coaching operate separately, so they miss the decision in its real context.

Direction taken

ELITE-PULSE separates facts from interpretation. Mobile collects context, the modular backend owns decisions and behavior, GraphQL is the public client contract, and the AI Mentor works on versioned profile and memory projections.

Business / Product

Problems and solutions

Each tile connects a real product tension with the decision that clarified the domain, UX, or operations.

Problem

Wellbeing apps, productivity tools, health data, and AI coaching operate separately, so they miss the decision in its real context.

Solution

Design a private loop: state, signal, decision, action, outcome, reflection, training, and AI interpretation.

Outcome Clear decision-intelligence category

Problem

The scope could easily become a feature list without one product, data, and ownership model.

Solution

ELITE-PULSE separates facts from interpretation. Mobile collects context, the modular backend owns decisions and behavior, GraphQL is the public client contract, and the AI Mentor works on versioned profile and memory projections.

Outcome GraphQL facade as the only client contract

Problem

The project needed decisions that would stay readable after the first version: for users, the team, and further delivery.

Solution

GraphQL facade as the only client contract. HealthKit/Health Connect as primary biometric paths.

Outcome Privacy architecture without medical claims

Roles and competencies

The competencies this build required

Competencies are shown as ownership roles: CTO, Tech Lead, engineering, DevOps, cloud, security, and AI where they were part of the work.

Fractional CTO

Connecting the product thesis, domain risk, and priorities so technology supports business decisions instead of becoming a separate workstream.

Open competency

Scope

Product category and positioning
Scope priorities and risk
Decisions ready for founder or CTO review

Applied experience

Product thesis translated into technical priorities.
Risks framed in a language stakeholders can review.

Tech Lead

Shaping responsibility boundaries, domain modeling, and architecture decisions so the project can grow without drifting into an accidental monolith.

Open competency

Scope

Bounded contexts and ownership
Readable technical decisions
Roadmap without implementation chaos

Applied experience

Domain boundaries and decisions that remain reviewable after MVP.
Ownership model readable for later delivery stages.

Software Engineer

Turning the domain into screens, APIs, flows, and maintainable implementation with focus on clarity and post-MVP evolution.

Open competency

Scope

Backend and application contracts
UX surfaces ready for iteration
Code and maintenance model

Applied experience

Implementation tied to the real product workflow.
Contracts and code prepared for post-launch iteration.

DevOps Engineer

Designing delivery, observability, and operations so release, diagnostics, and recovery are part of the product.

Open competency

Scope

CI/CD and release readiness
Observability and production signals
Operations without manual rituals

Applied experience

Release, diagnostics, and recovery designed as product capabilities.
Production signals ready for maintenance without guessing.

Cloud Engineer

Choosing cloud, data, and integration foundations so scale, cost, and reliability are not added after the fact.

Open competency

Scope

Google Cloud and managed services
Data, events, and storage
Cost and operational control

Applied experience

Cloud choices tied to cost, scale, and data responsibility.
Integrations and storage treated as foundations, not add-ons.

Security Engineer

Biometric data and reflections are encrypted as sensitive JSONB fields, authentication runs on OIDC, and the AI Mentor operates only on hashed, versioned projections - never on raw domain tables.

Open competency

Scope

Encrypted JSONB fields for biometrics, PII, and reflections
OAuth2/OIDC auth with facts kept separate from AI interpretation
OpenTelemetry tracing without PII in logs; the web app is internal backoffice only

Applied experience

Privacy, roles, and auditability built into the product model.
Security visible in domain decisions, not only around the UI.

AI/ML Engineer

Treating AI as a product capability: with context, constraints, versioned signals, and a clear boundary between facts and interpretation.

Open competency

Scope

LLM as a controlled capability
Signal and projection quality
Responsible product constraints

Applied experience

AI constrained by context, signals, and product responsibility.
Boundary between facts and interpretation preserved in architecture.

Stack by competency

Tech stack

The stack is grouped by competency so it shows both technology and responsibility: software, leadership, DevOps, cloud, security, and AI.

Software Engineer

.NET / ASP.NET Core
GraphQL
.NET MAUI mobile client
Angular web client
PostgreSQL

Tech Lead

Domain modeling
Identity governance
Analytics & projection boundaries

DevOps Engineer

Google Cloud
OpenTelemetry
Cloud Logging

Cloud Engineer

Google Cloud Platform

Security Engineer

Privacy architecture
OIDC
Consent-aware health data

AI/ML Engineer

LLM orchestration
Profile projections

.NET / ASP.NET Core .NET MAUI mobile client PostgreSQL Identity governance Google Cloud Cloud Logging Privacy architecture Consent-aware health data Profile projections GCP Health Connect Angular (internal backoffice) Encrypted JSONB fields .NET / ASP.NET Core .NET MAUI mobile client PostgreSQL Identity governance Google Cloud Cloud Logging Privacy architecture Consent-aware health data Profile projections GCP Health Connect Angular (internal backoffice) Encrypted JSONB fields .NET / ASP.NET Core .NET MAUI mobile client PostgreSQL Identity governance Google Cloud Cloud Logging Privacy architecture Consent-aware health data Profile projections GCP Health Connect Angular (internal backoffice) Encrypted JSONB fields

GraphQL Angular web client Domain modeling Analytics & projection boundaries OpenTelemetry Google Cloud Platform OIDC LLM orchestration .NET 8 HealthKit .NET MAUI OIDC authentication Versioned profile projections GraphQL Angular web client Domain modeling Analytics & projection boundaries OpenTelemetry Google Cloud Platform OIDC LLM orchestration .NET 8 HealthKit .NET MAUI OIDC authentication Versioned profile projections GraphQL Angular web client Domain modeling Analytics & projection boundaries OpenTelemetry Google Cloud Platform OIDC LLM orchestration .NET 8 HealthKit .NET MAUI OIDC authentication Versioned profile projections

Product preview

Gallery

Selected screens and materials showing the product surface, UX decisions, and work outcome.