Cover letters written for the job — with your real case studies

Paste a job description: the scenario finds the hidden screening questions, pulls your matching case studies, writes the letter and polishes it with a quality pass.

  • A letter for that specific job, not a template
  • Hidden screening questions are found and answered
  • Case studies are pulled from your own library
  • Three passes: draft, quality check, final polish
n8n FormPinecone Vector StoreEmbeddings OpenAI
Connect Postgres PGVector Store and launchScriptera

How it works

To connect Google Cloud Firestore and Postgres PGVector Store, you don't need a developer: a ready-made scenario links them in minutes.

  1. Starts when: Job Input Form
  2. Then: Job Details Analyzer
  3. Check: Merge: Cover + Job Details
  4. Then: Prepare for QC Review
  5. Then: Cover Quality Checker
  6. Check: Merge: QC Feedback + Cover
  7. Then: Prepare for Final Polish
  8. Then: Final Cover Polish
  9. Then: HTML Converter (Final Cover)
  10. Then: Save Final Cover to Docs
  11. Then: Screening Q&A Writer
  12. Then: Save Q&A to Docs
  13. Then: Cover Letter Generator
  14. Then: GPT-4 Turbo LLM
  15. Then: DeepSeek LLM (Q&A Writer)
  16. Then: DeepSeek LLM (Cover Writer)
  17. Then: DeepSeek LLM (QC Checker)
  18. Then: Claude 3.7 Sonnet LLM
  19. Then: Case Studies DB (Pinecone)
  20. Then: OpenAI Embeddings (Case Studies)
  21. Then: Ranking Keywords DB (Pinecone)
  22. Then: OpenAI Embeddings (Keywords)

You can launch this Google Cloud Firestore + Postgres PGVector Store integration in Scriptera: describe the task in plain words — the scenario is built, launched and monitored for you.

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