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How This Was Built

The design and delivery story behind the digital twin experience, from UI system to Terraform-backed deployment.

How This Was Built

Build Pipeline

01

Experience Design

Shared premium UI system, glass surfaces, consistent navigation, and branded gradients across every route.

02

3D Cosmic Engine

React Three Fiber scene with round stars, neural pulses, bloom, nebula layers, and motion tuned for cinematic depth.

03

AI Twin Interface

Client-side twin experience with voice input, Polly voice output, file uploads, scheduling flow, and controlled streaming UX.

04

Voice Layer (Amazon Polly)

Assistant responses are synthesized to lifelike speech with Amazon Polly (British male profile) and streamed back to the browser.

05

Infrastructure as Code

Terraform drives AWS resources, certificates, domains, storage, API edge routing, and deployment outputs.

06

CI/CD and Delivery

GitHub Actions builds, validates, deploys, and triggers CloudFront refresh for production delivery.

Build Sequence

Design system → 3D cosmic engine → AI twin interface → Bedrock + Polly integration → Terraform-defined backend → CI/CD automation → CloudFront production delivery.

Production Outcomes

Frontend

Next.js + React

3D Layer

R3F + Drei

Cloud Stack

Terraform + AWS

Frontend delivery

Browser

User visits the site over HTTPS

CloudFront

Global CDN, HTTPS termination, edge caching

S3 — Static Frontend

Next.js app, statically exported

Chat backend

API Gateway (HTTP API)

Routes /, /health, /chat, /tts, /contact, /schedule-meeting

Lambda — FastAPI

Python 3.12, Mangum adapter

Amazon Bedrock

Amazon Nova model, Converse API for chat reasoning

Amazon Polly

British male voice synthesis for assistant speech output

DynamoDB + SES

Contact capture, scheduling requests, and confirmation emails

CloudWatch

Operational metrics and telemetry for chat and scheduling flows

S3 — Memory Bucket

Per-session conversation history (JSON)

Infrastructure and delivery

Every resource above is defined as code in Terraform, with remote state in S3. Pushing to main triggers GitHub Actions that builds Lambda in a runtime-matched container, applies Terraform, rebuilds the frontend, syncs static output to S3, and invalidates CloudFront through short-lived OIDC credentials. The production assistant now combines Bedrock for reasoning and Polly for natural British male speech output.

Complete App Capabilities

  • • AI chat assistant powered by Bedrock contextual prompting
  • • Amazon Polly speech synthesis for assistant voice playback
  • • Browser speech-to-text for voice input and hands-free prompting
  • • File upload analysis workflow with contextual evidence injection
  • • Meeting scheduling and contact capture with backend persistence
  • • Session memory continuity and architecture-aware twin responses
  • • Premium 3D cosmic UX with animated neural network visual language

Deployment Runbook

  • • Code is versioned in GitHub with environment-aware Terraform variables.
  • • CI/CD packages Lambda in a runtime-matched container and applies infrastructure.
  • • Frontend static export is published to object storage and delivered through CDN.
  • • Backend API is exposed through HTTP edge routing with strict CORS controls.
  • • Observability comes from CloudWatch metrics/logs and deployment telemetry.

This architecture pattern is cloud-portable: static frontend + CDN, serverless API runtime, managed model endpoint, object storage for memory, and managed telemetry.

Contact Me

Let us design your next AI cloud system.

Share your timeline, architecture goals, and constraints. I respond directly by email.

Email luberguilarte@gmail.com