Genome AI is an AI-powered Enterprise Marketing Intelligence Platform that helps businesses make strategic decisions using AI agents, competitive ad intelligence, and brand analysis.
The platform provides a centralized AI command center for marketing, operations, and growth strategy, powered by modern cloud infrastructure and advanced AI models.
This project was built for an AWS Hackathon, integrating AWS cloud services while preserving the existing Next.js architecture.
Team Name: Code Rebels
Genome AI enables organizations to:
• Analyze competitor advertising strategies
• Generate AI-powered marketing insights
• Manage enterprise strategies with AI agents
• Generate professional intelligence reports
• Build data-driven brand positioning strategies
The platform acts as a strategic operating system for marketing teams.
Execute strategic decisions with 6 specialized AI agents:
• Sales Agent – Revenue optimization and sales strategy
• Marketing Agent – Campaign planning and brand positioning
• Finance Agent – Budget allocation and ROI analysis
• Operations Agent – Process optimization and efficiency
• Support Agent – Customer experience insights
• HR Agent – Talent strategy and organizational growth
Each agent generates structured recommendations, tasks, and insights.
AI-powered competitive ad analysis engine.
Capabilities include:
• Competitor Ad Analysis
• Instagram Creative Intelligence
• Design Pattern Recognition
• Color Psychology Analysis
• Typography Trend Insights
• Market Gap Detection
• AI Generated Ad Concepts
• Performance Predictions (CTR, Engagement)
• A/B Testing Recommendations
• PDF Intelligence Reports
Users can create a company profile with AI assistance, enabling the platform to provide personalized strategic recommendations.
Analyze the DNA of a brand including:
• Brand positioning
• Messaging structure
• Competitor landscape
• Market differentiation
Track and manage tasks created by AI strategies:
• Task status tracking
• Progress monitoring
• Strategy execution pipelines
Conversational AI assistant for:
• Marketing advice
• Campaign ideas
• Content strategy
• Brand messaging
Strategic recommendations require user confirmation before execution to ensure human oversight.
To support enterprise scalability and hackathon requirements, Genome AI integrates AWS services.
The architecture separates frontend, API, AI processing, and storage.
+----------------------+
| AWS Amplify |
| (Next.js Frontend) |
+----------+-----------+
|
|
v
+----------------------+
| API Gateway |
| REST API Layer |
+----------+-----------+
|
|
v
+--------------------+
| AWS Lambda |
| AI Processing |
| Strategy Agents |
| Report Generation |
+----------+---------+
|
+-------------------+-------------------+
| | |
v v v
+-------------+ +----------------+ +--------------+
| OpenAI API | | Amazon Bedrock | | DynamoDB |
| GPT-4o | | Claude 3 | | Strategy DB |
+-------------+ +----------------+ +--------------+
|
v
+----------------+
| Amazon S3 |
| PDF Reports |
| AI Assets |
+----------------+
| Service | Purpose |
|---|---|
| AWS Amplify | Deploy Next.js frontend |
| AWS Lambda | Serverless backend AI processing |
| API Gateway | REST API for Lambda |
| Amazon S3 | Store generated reports and assets |
| DynamoDB | Optional strategy/task database |
| Amazon Bedrock | Optional Claude 3 AI reasoning |
Frontend
• Next.js 16 (App Router)
• TypeScript
• Tailwind CSS
• shadcn/ui
Authentication
• Clerk
Database
• Supabase (primary)
• DynamoDB (optional AWS storage)
AI
• OpenAI GPT-4o
• DALL-E 3
• Amazon Bedrock Claude 3 (optional)
Reports
• jsPDF
Cloud
• AWS Amplify
• AWS Lambda
• Amazon API Gateway
• Amazon S3
Deployment
• Vercel (legacy option)
• AWS Amplify (AWS hackathon deployment)
Genome/
│
├── genome-nextjs
│ ├── app
│ │ ├── (dashboard)
│ │ │ ├── dashboard
│ │ │ │ ├── ad-intelligence
│ │ │ │ ├── enterprise
│ │ │ │ ├── chat
│ │ │ │ ├── genome
│ │ │ │ └── settings
│ │ │ └── layout.tsx
│ │
│ ├── api
│ │ ├── ad-intelligence
│ │ ├── enterprise
│ │ ├── chat
│ │ └── genome
│ │
│ ├── components
│ │ └── ui
│ │
│ └── lib
│ ├── ad-intelligence-pdf.ts
│ ├── meta-ad-library.ts
│ ├── together.ts
│ └── utils.ts
│
├── aws
│ └── lambda
│ ├── ad-intelligence
│ │ └── handler.ts
│ ├── ai-agents
│ │ └── handler.ts
│ └── report-generator
│ └── handler.ts
│
└── README.md
Create .env.local
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=
CLERK_SECRET_KEY=
NEXT_PUBLIC_SUPABASE_URL=
NEXT_PUBLIC_SUPABASE_ANON_KEY=
SUPABASE_SERVICE_ROLE_KEY=
OPENAI_API_KEY=
GOOGLE_API_KEY=
GOOGLE_SEARCH_ENGINE_ID=
META_AD_LIBRARY_TOKEN=
AWS_REGION=
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
S3_REPORT_BUCKET=
API_GATEWAY_URL=
BEDROCK_MODEL_ID=anthropic.claude-3-sonnet
import { APIGatewayProxyHandler } from "aws-lambda"
import OpenAI from "openai"
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY })
export const handler: APIGatewayProxyHandler = async (event) => {
const body = JSON.parse(event.body || "{}")
const { brand, competitor } = body
const response = await openai.chat.completions.create({
model: "gpt-4o",
messages: [
{
role: "user",
content: `Analyze advertising strategy between ${brand} and ${competitor}`
}
]
})
return {
statusCode: 200,
body: JSON.stringify(response.choices[0].message)
}
}import { APIGatewayProxyHandler } from "aws-lambda"
import OpenAI from "openai"
const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY
})
export const handler: APIGatewayProxyHandler = async (event) => {
const { agent, task } = JSON.parse(event.body || "{}")
const result = await openai.chat.completions.create({
model: "gpt-4o",
messages: [
{
role: "system",
content: `You are the ${agent} agent in an enterprise strategy system`
},
{
role: "user",
content: task
}
]
})
return {
statusCode: 200,
body: JSON.stringify(result.choices[0].message)
}
}import { S3Client, PutObjectCommand } from "@aws-sdk/client-s3"
import jsPDF from "jspdf"
const s3 = new S3Client({ region: process.env.AWS_REGION })
export const handler = async (event:any) => {
const { report } = JSON.parse(event.body)
const pdf = new jsPDF()
pdf.text(report, 10, 10)
const buffer = Buffer.from(pdf.output("arraybuffer"))
const key = `reports/report-${Date.now()}.pdf`
await s3.send(new PutObjectCommand({
Bucket: process.env.S3_REPORT_BUCKET,
Key: key,
Body: buffer,
ContentType: "application/pdf"
}))
return {
statusCode: 200,
body: JSON.stringify({
url: `https://${process.env.S3_REPORT_BUCKET}.s3.amazonaws.com/${key}`
})
}
}const response = await fetch(
`${process.env.NEXT_PUBLIC_API_GATEWAY_URL}/ad-intelligence`,
{
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(data)
}
)
const result = await response.json()- Open AWS Amplify
- Connect GitHub repository
- Select branch
- Build settings
npm install
npm run build
- Deploy
cd aws/lambda/ad-intelligence
npm install
zip -r function.zip .
aws lambda create-function ...
1 Create REST API
2 Create route
POST /ad-intelligence
3 Connect Lambda
Create bucket:
genome-ai-reports
Enable:
• public read access
• versioning (optional)
• Clerk authentication
• Secure API routes
• AWS IAM policies for Lambda + S3
• Environment variable protection
• Real-time analytics dashboard
• Multi-agent orchestration
• Vector search for marketing insights
• Full AWS Bedrock integration
• Automated campaign generation
MIT License
Next.js
AWS
OpenAI
Supabase
Team Code Rebels
AI-Powered Enterprise Marketing Intelligence