Public AI product

MetaSculpt AI

I co-founded and shipped a public AI product spanning prompt tracking, citation capture, crawl analysis, weekly reports, and Schema JSON generation.

Public Work Shipped 2024 - 2025
The story behind it Visit MetaSculpt
MetaSculpt interface showing an action brief, visibility chart, account navigation, and login surface
Public MetaSculpt interface. I co-founded the product and owned AI workflow design, crawler and reporting logic, documentation, and code review with a co-founder.
  1. Crawl
  2. Generate
  3. Validate
  4. Fix
The shipped product kept crawl collection, structured generation, validation, and corrective action in separate stages.

Problem

What needed to change

People trying to compete in AI search needed a practical way to track visibility in AI-generated answers, understand which prompts mentioned them, compare competitor citations, and add useful Schema JSON to their sites.

Users

Who the system serves

Site owners, marketers, and AI-search visibility teams trying to understand why they appeared or failed to appear in GPT, Perplexity, Gemini, and other AI-answer surfaces.

Team context

Where I fit

Co-Founder (technical lead)

Two-person company. Product and technical decisions were shared with a co-founder; I owned AI workflow design, crawler and reporting logic, documentation, and code review.

Important technical decision

Separate collection from generation

I separated crawler and generator responsibilities and organized the product around a Crawl → Generate → Validate → Fix flow.

Tradeoff or limitation

Delivery without traction

A Wasp frontend template was the starting point. The product shipped publicly but did not gain user traction, so the verified result is delivery rather than adoption.

Implementation

What I personally owned

  • Owned the AI workflow design, crawler logic, prompt tracking, experiment setup, weekly reporting, documentation, and code review with a co-founder.
  • Used a Wasp frontend template as the starting point, then implemented the product workflow, data capture, report logic, and AI-search analysis around it.
  • Shared product and technical decisions with the co-founder.

Result

Result and limitations

  • Prompt and experiment tracking with competitor and citation capture.
  • Crawler-backed site comparison and weekly insight reports.
  • Schema JSON generation and validation.
  • The complete workflow shipped publicly.
  • The product did not ultimately gain user traction; the verified result is delivery, not adoption.

Technical scope

Tools used

OpenAI APIGemini APISchema.orgREST APIsStructured metadataValidation workflowsWaspTypeScriptDocker