Building Wizy Marketing AI on AWS: From Concept to Global Scale
A guide to building Wizy Marketing AI on AWS: From S3 storage and RAG with Bedrock to agentic orchestration with LangGraph on Lambda.
For a startup in its breakout phase, choosing a robust and stable cloud infrastructure is a make-or-break decision. At Wizy, we rely on the AWS ecosystem to realize our vision of a smart, globally scalable Marketing AI platform.
1. The AWS Build Process for Wizy
Step 1: Brand Knowledge Preparation
The first step is helping the system "understand" client data (websites, past posts, product docs).
- Tools: Amazon S3 for raw storage and Amazon SageMaker Data Wrangler for data cleansing.
- Goal: Creating a clean "data lake" for high-accuracy AI learning.
Step 2: Intelligent RAG (The Memory)
Converting raw data into searchable vectors for instant semantic retrieval.
- Tools: Knowledge Bases for Amazon Bedrock.
- Vector Database: Amazon OpenSearch Serverless (auto-scaling, zero-ops).
- Embedding Model: Amazon Titan Text Embeddings v2.
Step 3: Marketing Agents (The Brain)
Where top-tier LLMs drive creative writing and campaign strategy.
- Tools: Agents for Amazon Bedrock.
- LLM Core: Claude 3.5 Sonnet (for premium marketing copy) or Llama 3 70B.
- Orchestration: LangGraph running on AWS Lambda to enable self-improving workflows (Write -> SEO Check -> Polish).
Step 4: Fine-tuning & Optimization
To give the AI a signature "Wizy voice" beyond what RAG can provide.
- Tools: Amazon SageMaker JumpStart.
- Action: Fine-tuning Llama 3 on our curated dataset of high-performing marketing content.
Step 5: Frontend & Deployment
- Frontend: Next.js deployed on AWS Amplify or Amazon ECS (Docker).
- API Gateway: Using Amazon API Gateway to manage secure app-to-AI connections.
2. Strategic Advantages in Canada
Operating in the Canada (Central) region (ca-central-1) provides key benefits:
- Data Residency: Ensuring Canadian client data stays within national borders to meet strict security compliance.
- AWS Activate: Leveraging startup credits to fuel early-stage PoC development and scale efficiently.
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