All case studies Matchmaking · Discovery

A matchmaker got a repeatable pipeline for sourcing candidates from public rosters

A head-to-head benchmark across 5 AI systems on 69 real candidates produced a copy-paste pipeline plus a hallucination-detection guide.

Industry
Matchmaking
Engagement
Discovery
Timeline
One-shot research project
Outcomes
Knowledge unlocked

The problem

A matchmaker needed a repeatable workflow: given a public candidate page (a university athletic roster, a professional directory), find each woman's LinkedIn, Facebook, and Instagram profiles and produce a structured spreadsheet with photos. Question: which AI tool is best at this?

What we built

  • Cross-tool benchmark across Claude Sonnet 4.6, Claude Opus 4.6, Perplexity Pro Deep Research, Manus AI, and Gemini
  • Tested on 69 real candidates from two public rosters
  • Self-contained benchmark report (HTML) with side-by-side accuracy scores
  • Recommended Best Combined Pipeline: Sonnet for scrape → Perplexity for LinkedIn → Manus for Instagram and Facebook → Perplexity 2nd pass for zero-link → Sonnet to merge
  • Projected coverage: LinkedIn 75-80%, Facebook 25-30%, Instagram 65-70%, at least one link 90%+
  • Hallucination detection guide flagging specific failure modes (Gemini fabricates LinkedIn slugs with fake hex IDs)

Stack

Claude Sonnet 4.6Claude Opus 4.6Perplexity ProManusGemini

Outcome

Benchmark complete. Recommendations delivered and in production use.

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