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Kalibrate — Two joined assets: (1) a daily station-level fuel price panel — 600,000+ price observations delivered daily across retail forecourts with competitor positions; (2) a geospatial location-intelligence layer — 100+ market attributes per site (catchment demographics, traffic, competition, cannibalisation geometry) plus fuel-volume/c-store/QSR feasibility forecasts, network scenario outputs and price-optimisation decisions (AI recommendations with brand-rule overrides). Large niche data platform: 330 employees, ~$16M revenue band, Manchester HQ with global operations (UK/US), one of th…
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Two joined assets: (1) a daily station-level fuel price panel — 600,000+ price observations delivered daily across retail forecourts with competitor positions; (2) a geospatial location-intelligence layer — 100+ market attributes per site (catchment demographics, traffic, competition, cannibalisation geometry) plus fuel-volume/c-store/QSR feasibility forecasts, network scenario outputs and price-optimisation decisions (AI recommendations with brand-rule overrides)
Scale Stated: 600From 12 yearsCoverage Energy · Information Technology
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Kalibrate — Two joined assets: (1) a daily station-level fuel price panel — 600,000+ price observations delivered daily across retail forecourts with competitor positions; (2) a geospatial location-intelligence layer — 100+ market attributes per site (catchment demographics, traffic, competition, cannibalisation geometry) plus fuel-volume/c-store/QSR feasibility forecasts, network scenario outputs and price-optimisation decisions (AI recommendations with brand-rule overrides). Large niche data platform: 330 employees, ~$16M revenue band, Manchester HQ with global operations (UK/US), one of th…
Kalibrate offers (Alternative, Price, Geospatial, Consumer) — Stated: 600,000+ fuel prices delivered daily (order 2x10^8 price observations per year) plus 100+ market attributes per site/trade-area polygon; joined history over multi-year retention makes this a high-volume panel.
Fuel dynamic-pricing and elasticity model training on the daily price + volume panel; forecourt price-index and inflation nowcasting; retail real-estate site-selection and cannibalisation models (the location layer is the scarce asset); retail real-estate valuation with catchment attributes; mobility/traffic demand modelling from trade-area attributes; network-planning RL environments (scenario simulation data); training governed-AI pricing agents on recommendation-vs-override pairs
The data is with 12 years of history.
Coverage spans US, UK; Oil & Gas Refining and Marketing, Information Technology Services; alternative, price, geospatial, consumer; equities, commodities.
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