Landlord Tenant Risk Screener

AI Prompts to Build This

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1. 1. Project Setup

   Build the weekend MVP of "ProofCheck": a small landlord uploads an applicant's documents and gets a short list of things worth checking before signing a lease. It supports a decision. It never approves, denies or scores a person.
   
   Stack: Next.js (App Router), TypeScript, Tailwind, Supabase (Postgres, Row Level Security, Storage, Auth with email magic link), the Anthropic API to read pay stubs, pdf-parse and pdf-lib to read text and metadata. The checks that can be done in code are done in code. Deploy on Vercel.
   
   Tables (Row Level Security on, a landlord reads only their own rows):
   - properties(id, user_id, address, rent_cents)
   - applicants(id, property_id, label, claimed_income_cents, claimed_employer) label is a short tag such as "Applicant A"; no other personal details are stored
   - packets(id, applicant_id, file_path, sha256, uploaded_at, doc_kind) uploaded_at is set by the server
   - findings(id, packet_id, kind, severity, evidence_quote, explanation, human_check, confidence) kind is metadata, income, employment or inconsistency; severity is info, warn or high
   
   Screens: /login, /properties, /applicants/[id] (upload and the findings card), /applicants/[id]/checklist (printable).
   Env vars (names only): NEXT_PUBLIC_SUPABASE_URL, NEXT_PUBLIC_SUPABASE_ANON_KEY, SUPABASE_SERVICE_ROLE_KEY (server only), ANTHROPIC_API_KEY.
   
   Do not build: billing, plans or a portfolio tier, a composite score, ranking or comparing applicants, public records or eviction searches, credit data, automatic rejection.
   Done when: npm run dev starts, you can sign in, and the four tables exist with Row Level Security on.

2. 2. Core Feature

   Build the one feature that proves ProofCheck: flags that quote their evidence and say what a human should verify.
   1. Upload a PDF (pay stub, bank statement or employment letter) to a private bucket. Compute and store its sha256 and set uploaded_at on the server.
   2. In code, read the PDF metadata and text: producer and creator tools, creation and modification dates, page count and font names. Raise a flag when the modification date is later than the creation date by more than a day, when the file was created within 2 days before the upload, or when a one-page stub uses more than 3 font families.
   3. For pay stubs, send the text to the Anthropic API with a strict JSON schema { employer_name, gross_per_period, pay_frequency, ytd_gross, quotes: string[] }. Instruct it to ignore names, addresses, age and any family or protected detail, and to use only figures in the document.
   4. Check each quote in code: it must appear in the extracted text, or the field is dropped. Compute in code the income implied by the stub, its difference from claimed_income_cents, and the rent to income ratio.
   5. Save findings. Each has evidence_quote or the metadata value, a plain explanation and a human_check line such as "Call the employer on a number you find yourself, not the one on the stub".
   6. Render a card of 3 to 7 bullets, each tied to a finding, then a printable verify-before-lease checklist. Every card carries: "Decision support only. Not a consumer report. Not legal advice. The landlord makes the housing decision."
   Rules: never output approve, deny or a score. Log every model response for later review. A Delete applicant button removes the files and findings.
   Empty state: a new applicant shows the upload box and the list of documents that help.
   Done when: a test PDF edited after creation raises the metadata flag, a stub whose income differs from the claim by over 10 percent raises an income flag with a real quote, a fabricated quote is dropped, and no screen shows approve, deny or a score.

3. 3. Landing Page

   Build a one-page landing site for ProofCheck, document checking for small landlords.
   Hero: "Check the story, not just the score." Sub: "Upload the application and pay stubs. Get a short list of things worth verifying before you hand over the keys. You make the decision." One button: Join the waitlist.
   Sections: a scene (a patched PDF and a phone number that goes nowhere), a sample findings card with an evidence quote and a "verify this" line, how it works in four steps (upload, check, review, verify), and a trust block (decision support only, not a consumer report, no approve or deny, no scoring of people). An FAQ on what is stored, how to delete an applicant and why a lawyer should confirm how housing rules apply to you.
   Waitlist: store the email in a waitlist table in Supabase. No other service.
   Style: Geist, a dark desk-like background, one aubergine accent, no people and no unreadable interface text.
   Done when: the page renders on a phone and a submitted email appears in the waitlist table.

4. 4. Branding Package

   Use a design or image tool for this one. A coding agent cannot draw a logo.
   Brand for ProofCheck: a wordmark and an icon that suggest a document with a magnifier over one line.
   Colors: deep ink, off-white and one aubergine. Type: Geist, with IBM Plex Mono for quoted evidence.
   Deliverables: wordmark, icon, three finding chips (info, check, high) that differ by shape as well as color, a findings card layout that always shows the decision-support line, and one launch graphic.
   Done when: each deliverable is saved in one folder and the finding chips are distinguishable without color.