Algeria commercial intelligence terminal
StreetIQ DZ
Evaluate Algerian streets, neighborhoods, communes, and wilayas with opportunity heatmaps, public evidence, confidence scoring, credit-based reports, and operational risk layers.
Built for Algerian location decisions
The platform connects map data, public-source evidence, scoring logic, and review workflows into one operating system.
Map-first analysis
Click a street, drag a pin, compare districts, and inspect commercial context without leaving the map.
Backend heatmaps
Opportunity, risk, demand, confidence, rent pressure, and delivery layers are generated server-side.
Evidence-backed reports
Every generated report carries score components, confidence, limitations, and public evidence cards.
Algeria language engine
French, Arabic, Darja, and Arabizi normalization for messy public-source location signals.
Credits and billing
Subscriptions, credit ledger, top-ups, paid report generation, API access, and team workspaces.
Admin review
Evidence, aliases, geocoding, source operations, scoring models, heatmaps, audit logs, and coverage.
Use cases
Entrepreneurs
Choose a shop location, avoid rent traps, compare streets, and generate investor-ready reports.
Commercial real estate
Prove commercial potential and recommend the right tenant mix for properties.
Distributors
Find underserved zones, plan sales routes, and track opening or closure signals.
Delivery companies
Understand address ambiguity, access friction, parking difficulty, and delivery risk.
Landlords
Reduce vacancy by matching property context to business categories.
Consultants
Build evidence-backed market studies and repeatable location workflows for clients.
Plans and credits
For first location decisions
- Limited map access
- Starter heatmaps
- 10 report credits
- Saved locations
For recurring operators
- Advanced heatmaps
- PDF exports
- 50 report credits
- Watchlists and alerts
For teams and agencies
- Team seats
- Bulk reports
- API trial
- Shared workspace
For institutional intelligence
- Custom layers
- SLA
- Private data integration
- Dedicated onboarding
No fake precision
Sparse regions are intentionally marked as low-confidence area estimates.
Sensitive claims are neutral
The platform reports public risk signals, not personal accusations.
Source-driven confidence
Confidence rises only when map, evidence, and review signals support the claim.