bi.cafe
bi.cafe is a market-intelligence platform that turns public places, reviews, and location signals into structured datasets — so expansion, competitive, and research teams can decide with evidence, not guesswork.
Most location decisions still rely on manual Google Maps searches, one-off exports, and incomplete API coverage. bi.cafe automates the collection layer: you define a city, category, or trade area, and the platform gathers places, review text, ratings, hours, photos, and metadata at scale — ready for maps, spreadsheets, models, and internal tools.
What it answers
- Site selection — compare density, ratings, and review patterns across neighborhoods before committing to a location.
- Competitive benchmarking — see who dominates a category, how they are rated, and what customers say, market by market.
- Underserved markets — spot gaps in coverage, weak competition, and pockets of demand others have not mapped yet.
- Sentiment at scale — read review text across thousands of venues — themes, praise, complaints, and shifts over time.
- Research datasets — export structured places and reviews for GIS, urban analytics, NLP pipelines, and custom models.
How it works
- Define the market — pick a city, category, or drawn boundary.
- Parallel collection — jobs run across the full trade area, gathering place details and review text beyond what public APIs typically expose.
- Structured output — explore in the app or download CSV/JSON datasets with no manual cleaning required.
Data model
Each completed job returns structured records including:
- Places — name, address, coordinates, category, hours, rating, review count, and photos.
- Reviews — full text, dates, star ratings, and reviewer metadata.
- Coverage — city-scale or category-scale runs with metered, pay-per-success pricing.
Pricing is usage-based: points are deducted only when a place finishes successfully — no subscription, no charge for failures.
Who it’s for
Retail and F&B expansion teams, real-estate and site-selection analysts, market-intelligence groups, local SEO agencies, and academic researchers working with location-based data.
Built and operated alongside my work at 4dt.io. Singapore-based.