You are building the Quantum Environmental Intelligence System (QEIS).

Purpose:
Develop an AI-driven system that fuses quantum-inspired simulation, environmental data sensing, and psychophysiological feedback to map and optimize the unseen patterns of human–environment coherence. The system combines real-world sensor input, AR visualization, and quantum-mechanical analogs for predictive modeling and environmental intelligence.

Core Objectives:
- Capture environmental and biometric data in real time.
- Simulate probability and coherence fields using quantum-inspired algorithms.
- Visualize AR overlays showing energetic or environmental flow.
- Generate actionable insights for sustainability, design, and wellness.

System Architecture:
1. Mobile Layer:
   - React Native with ARCore/ARKit for AR environment rendering.
   - TensorFlow Lite models for local predictions and biofeedback analysis.
   - Sensor API integration (camera, GPS, EMF, accelerometer, microphone, EEG).

2. Backend:
   - Node.js + Express + WebSocket for live data streaming.
   - REST + MQTT endpoints for IoT and bio-sensor input.
   - PostgreSQL (user + metadata) + InfluxDB (time-series environment data).
   - Redis or Kafka for event streaming and message queues.

3. Cloud AI Pipeline:
   - Vertex AI for large-scale model training and deployment.
   - TensorFlow and PyTorch pipelines for pattern recognition.
   - AutoML for coherence mapping and anomaly detection.
   - Synthetic Data Generator (Python + NumPy + Faker) to simulate diverse sensor environments.

4. Visualization Layer:
   - Next.js frontend deployed on Vercel.
   - Three.js + WebGL quantum-field rendering.
   - Real-time “coherence map” overlay using probabilistic color gradients.
   - Optional neural interface visualization (EEG, HRV, or GSR streams).

API Schema:
- POST /data/sensor
  → { device_id, timestamp, type, value, geo }
- GET /map/coherence
  → returns AR field JSON grid + vector overlays
- GET /forecast/anomaly
  → predictive anomaly report
- WS /stream
  → live stream for visualization

Database Structure:
- users(id, name, auth_id, permissions)
- devices(id, user_id, type, last_seen)
- sensor_data(id, device_id, sensor_type, value, timestamp, geo)
- coherence_map(id, timestamp, grid_data, model_version)

MQTT Topics:
- qeis/sensors/[device_id]
- qeis/predict/anomaly
- qeis/visual/field

WebSocket Message Example:
{
  "type": "coherence_update",
  "payload": {
    "region": "local",
    "coherence_index": 0.87,
    "timestamp": "2025-11-05T12:00:00Z"
  }
}

Machine Learning Tasks:
- Train environmental coherence models via Vertex AI.
- Convert TensorFlow models to TFLite for mobile inference.
- Use synthetic data generator to augment training dataset.
- Apply unsupervised clustering for quantum-like entanglement mapping.

TF Lite Conversion Script:
python
import tensorflow as tf
model = tf.keras.models.load_model('model.h5')
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("model.tflite", "wb").write(tflite_model)

Terraform Template (GCP):
terraform
provider "google" {
  project = "qeis-project"
  region  = "us-central1"
}
resource "google_storage_bucket" "qeis_data" {
  name     = "qeis-data-bucket"
  location = "US"
}
resource "google_vertex_ai_endpoint" "qeis" {
  display_name = "qeis-endpoint"
}

GitHub Actions (CI/CD):
yaml
name: Deploy QEIS
on: [push]
jobs:
  build-deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - run: npm install && npm run build
      - run: vercel deploy --prod

Sprint Plan (8 Weeks):
Week 1–2: Backend scaffolding, API routes, DB setup
Week 3–4: Frontend UI, AR rendering, WebSocket integration
Week 5–6: ML pipeline, Vertex AI model training
Week 7: TFLite integration + mobile tests
Week 8: Cloud deployment + system test

Immediate Setup Commands:
# Clone and initialize repo
git clone https://github.com/yourname/QEIS.git
cd QEIS
npm install
vercel link
firebase init
gcloud init
terraform init && terraform apply

Output:
Fully operational, AI-powered, AR-integrated quantum environmental intelligence platform ready for GitHub deployment, Google Vertex AI integration, and open interoperability with other edge and cloud services.