Enterprise-grade location intelligence combining government data, live web data, and ML-driven scoring — calibrated to your portfolio and competitive landscape. Multi-tenant, role-based, and ready to scale.
From interactive choropleth maps to ML-powered revenue forecasting, every module is designed to shorten the path from data to decision.
KPI overview & priority heatmap
Choropleth + 8 layers across 5 region levels
Top expansion sites, composite-scored
Per-kelurahan detail with isochrones
MAP & MAA portfolio gaps by region
Anchored-mall analysis + tenant audit
833+ competitor stores scraped live from OSM
Weight comparison & sensitivity analysis
Gradient Boosted Regressor for revenue
PDF / CSV / JSON export, scheduled
CRUD on stores, malls, brands, POIs
Auto-scrape OSM POIs into staging tables
Scoring framework, math, validation
Searchable guides & API references
Project overview & data sources
Per-user AI config & preferences
OSM scraper + BPS government data + human-curated master data on stores, malls, brands, and competitors — all in one PostGIS database with RLS isolation per tenant.
6-factor composite opportunity score (0–100) per kelurahan, plus Gradient-Boosted Regression models trained on actual outlet performance to project monthly revenue, market share, and cannibalization risk.
Interactive choropleth maps, deep-dive per-kelurahan analysis, A/B weight simulator, and PDF/CSV export — everything your expansion team needs to pick the next 100 store locations with confidence.
From a single tenant in the cloud to a fully white-labeled on-prem installation — pick the option that fits your team and scale.
Fastest way to go live. We host, monitor, and update the platform; you focus on expansion decisions.
Deploy LocInsights inside your own VPC. Full source access, custom integrations, unlimited users.
Already have a BI stack? We embed LocInsights capabilities into your existing tools, or build bespoke modules.
Sign in with your demo account and explore the full platform — 709 kelurahan, 833 competitors, 120+ brands, and ML-powered opportunity scoring.