Review-mining tool for Airbnb hosts

Micro-SaaSweekendWeb

StackNext.jsClaude API

Search demandLowFew people are searching for this.
CompetitionLowThere are few direct competitors.
Customer valueLowCustomers are likely to spend a little.
How scoring works

The idea

Airbnb hosts live and die by their rating but Airbnb gives them zero analysis — just a wall of review text. A host with 200 reviews has no idea that “slow wifi” appears in 7 of them. LLMs make this a weekend build: extraction, clustering, and counting over unstructured text is exactly what they’re good at.

Who pays

Hosts and small property managers, especially anyone below 4.8 stars fighting for Superhost status. $19 per report or $9/month for tracking. Property managers with 20 listings are the whale customers.

MVP scope

  • Paste-in reviews (no scraping), analyze with one well-designed prompt
  • Report page with complaints ranked by frequency and severity
  • Shareable report link + Stripe one-time payment
  • Skip for v1: auto-sync with listings, competitor comparison, VRBO/Booking.com
// Build prompt — paste into Cursor / Claude Code / Lovable / Bolt
Build HostLens: a tool that turns Airbnb reviews into an actionable
improvement report for hosts.

Core flow: a host pastes their listing's reviews (v1: a big textarea plus
a "paste from clipboard" helper — do NOT scrape Airbnb). The app sends the
reviews to the Claude API (model: claude-sonnet-5) with a prompt that
extracts every distinct complaint and compliment, clusters them into
themes (cleanliness, check-in, wifi, noise, accuracy, amenities, host
communication), counts mentions per theme, pulls representative quotes,
and flags severity. Ask the model to return strict JSON matching a schema
you define, then render it.

Report page: a summary verdict at top ("Your reviews are 86% positive;
the #1 fixable issue is wifi speed, mentioned in 7 reviews"), a
complaints table sorted by frequency x severity with quotes, a strengths
section (what to emphasize in the listing description), and a suggested
action list. Include a "copy report" button and a shareable read-only
link.

Stack: Next.js (App Router) + TypeScript, Tailwind, Anthropic SDK on a
server route, Supabase for saved reports and auth, Stripe for a $19
one-time "full report" (free tier analyzes 10 reviews).

Write the extraction prompt carefully: instruct the model to only cite
things actually present in reviews, never invent issues, and include
reviewer quotes verbatim.

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