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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
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Flame Analytics
✓ verifiedPaid
Privacy-first AI video analytics for stores and venues, measuring traffic, conversion, dwell time, and occupancy from existing CCTV.
36K visits/mo
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BasicAI Cloud
✓ verifiedFreemium
Data-annotation platform with AI-assisted labeling tools and team workflows for building ML training datasets.
22K visits/mo
Pricing
No public pricing
Pay-as-you-go: 42.8% platform fee for corporate, 33.3% for academic/non-profit (no monthly fee)
Participant payment: minimum $8.00/hr, recommended $12.00/hr
No public pricing
No public pricing
Core features
- ✦AI-powered product review analysis
- ✦Sentiment analysis in Azerbaijani
- ✦Topic analysis in Azerbaijani
- ✦Social listening platform
- ✦Access to a verified and engaged participant pool
- ✦Self-serve platform for easy task setup and launch
- ✦Tools for AI training and evaluation
- ✦Fair compensation for participants
- ✦Audience checker
- ✦AI people counting (no biometrics)
- ✦Conversion analytics cross-checked with POS
- ✦Customer journey and heatmaps
- ✦Real-time occupancy and queue analytics
- ✦Guest WiFi capture and marketing
- ✦Customizable dashboards, 200+ KPIs
- ✦AI-assisted data annotation tools
- ✦Training-data platform (BasicAI Cloud)
- ✦Team and project management
- ✦Annotation services
Use cases
- →Analyzing product reviews to improve brand understanding
- →Driving conversions through data-driven insights
- →Monitoring social media for brand mentions in Azerbaijani
- →Understanding customer sentiment towards products in Azerbaijani
- →Academic research
- →AI training and evaluation
- →Market research
- →User research & testing
- →Data annotation
- →Training & alignment
- →Evaluation & safety
- →Measuring footfall and conversion in stores
- →Optimizing mall tenant mix and layout
- →Activating marketing via guest WiFi
- →Labeling images and data for ML models
- →Managing annotation teams and projects
- →Producing training datasets at scale
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