BrandPilot: AI Marketing Agency on Autopilot

An AI-powered marketing agency that runs overnight—tracking competitors, reviews, and opportunities, then delivering actionable insights automatically.

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Inspiration

Small businesses spend 15–20 hours every week manually tracking competitors, reading reviews, and searching for opportunities. Hiring a professional marketing agency can cost thousands of dollars per month, making it inaccessible for most.

We wanted to explore: What if a team of AI agents could do the same work—continuously, automatically, and at near-zero cost?

BrandPilot was built to turn that idea into a real, working system.

What it does

BrandPilot is an AI-powered marketing intelligence platform that runs autonomously in the cloud.

It uses a fleet of specialized agents:

  • Competitor Scout – tracks competitor pricing and promotions
  • Review Guardian – monitors customer reviews across platforms
  • Mention Tracker – scans online discussions and news
  • Opportunity Hunter – identifies grants, partnerships, and growth opportunities

These agents run on a schedule, collect data in parallel, and feed it into an AI system that generates a weekly intelligence report. The report is automatically delivered to the user, helping them make informed business decisions without manual effort.

How we built it

We built BrandPilot as a full-stack, cloud-native system:

  • Frontend: React + TypeScript dashboard for a unified command center
  • Backend: FastAPI for API services and orchestration
  • AI & Agents: Amazon Nova Act with AgentCore Browser for real-time web automation
  • Intelligence Layer: Amazon Nova 2 Lite via Bedrock for report generation
  • Infrastructure: AWS Lambda, S3, SNS, and EventBridge for scheduling, storage, and notifications
  • Deployment: AWS SAM for serverless infrastructure management

The system supports both mock and live execution modes, enabling reliable demos as well as real-world operation.

Challenges we ran into

  • Geo-restrictions: Nova Act playground access was limited in our region, so we implemented everything using the Python SDK and AWS CLI.
  • Reliable automation: Ensuring browser agents could consistently extract structured data from dynamic websites required careful handling and fallback logic.
  • Parallel orchestration: Managing multiple agents running simultaneously while maintaining stability and performance was complex.
  • Resilience: We designed hybrid execution modes (live + fallback) to ensure the system remains functional even when external services fail.

Accomplishments that we're proud of

  • Successfully built and deployed a fully working AI agent system on AWS
  • Achieved end-to-end automation: data collection → analysis → report → email delivery
  • Ran real agent workflows and received live SNS notifications with generated insights
  • Designed a scalable architecture that can support multiple users and continuous execution
  • Created a product that directly addresses a real-world business problem

What we learned

  • Building reliable AI systems requires strong fallback mechanisms and observability
  • Cloud-native architectures (Lambda, EventBridge, SNS) enable powerful automation with minimal overhead
  • Multi-agent systems can significantly improve task coverage and depth compared to single-model approaches
  • Real-world constraints (like API limits and geo-restrictions) shape system design more than expected

What's next for BrandPilot: AI Marketing Agency on Autopilot

  • Add real-time alerts for critical business changes (e.g., competitor price drops)
  • Introduce actionable recommendations (not just insights, but what to do next)
  • Expand integrations with more platforms (social media, ads, analytics tools)
  • Improve personalization based on business type and industry
  • Enable collaborative dashboards for teams

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