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A personal startup concept for an AI-driven Chrome extension that helps Australian first-home buyers organise property research and compare evidence.

  • Figma
  • Chrome Extension
  • Vanilla JS
  • React
  • Supabase
  • Python
  • AWS Lambda
  • Docker
  • Terraform
  • Astro
  • Netlify
Live DemoGitHub

FirstHomeMatch is a Figma-stage startup concept for Australian first-home buyers. A Chrome extension and companion dashboard organise listing information, explain unfamiliar details, preserve source links, and compare saved properties. AI supports research but does not replace professional advice.

The project is currently focused on product discovery and interface design, not a launched service.

  • Current work covers user journeys, information hierarchy, Figma screens, and technical planning.
  • AI will organise research, while users verify sources and consult qualified Australian professionals.
  • Scope, integrations, data sources, and the commercial model still require validation.

Australian first-home buyers often compare fragmented information across listings, calculators, reports, notes, and professional advice.

  • Primary users are people preparing to buy their first owner-occupied Australian home.
  • The extension turns the current listing into a structured workspace for questions, notes, and evidence.
  • Consistent fields make saved properties easier to compare than memory or separate spreadsheets.

Listing Page to Research Workspace

Browse an Australian property listing
Open the FirstHomeMatch Chrome extension
Supported page and user permission?

No

Explain the limitation and allow manual entry

Yes

Capture user-visible property fields
User checks and corrects the structured property record
Request an AI-assisted research summary
Show plain-language insights, questions, and source links
Save to a FirstHomeMatch account?

Not yet

Continue reviewing the current listing

Save

Open the React workspace to compare properties

Users review a listing in place, capture structured evidence, then compare saved properties in the dashboard.

Each interface uses a stack suited to its scope.

  • Claude Code supports development but is not part of the customer-facing runtime.
  • Vanilla JavaScript keeps the Chrome extension close to browser APIs; React supports the stateful dashboard.
  • Astro and Netlify provide a lightweight, Git-deployed marketing site with CDN delivery.

The backend separates product data, lightweight functions, and heavier processing.

  • Supabase provides authentication, Postgres, storage, full-text and vector search; Edge Functions handle lightweight workflows.
  • Python and Docker package heavier AI and data jobs for AWS Lambda or ECS Fargate, coordinated by Step Functions and EventBridge.
  • Terraform keeps AWS provisioning repeatable and reduces manual configuration.

The architecture is a plan, not a production-validated implementation.

  • Complete and test the core Figma flows with buyers and Australian property professionals.
  • Confirm lawful data sources, portal compatibility, permissions, consent, and retention rules.
  • Prototype editable extraction and one cited, human-verified AI workflow before expanding features.
  • Validate cost, latency, security, and scaling needs before adopting the full cloud stack.