Aerocloudsystems

Name: Minute-by-minute terminal-wide passenger flow and queue telemetry from live airports: queue length and wait time at each processing point (check-in, security, immigration, boarding), footfall and dwell-time patterns, passenger hotspot locations, crowding events, and the SLA/congestion alert log. Positioned as a single curb-to-gate view, so the series span the full journey rather than one checkpoint, and dwell-time data is explicitly captured as a retail-revenue input
Creator: Aerocloudsystems

AEROCLOUDSYSTEMSManage supplier listing

Minute-by-minute terminal-wide passenger flow and queue telemetry from live airports: queue length and wait time at each processing point (check-in, security, immigration, boarding), footfall and dwell-time patterns, passenger hotspot locations, crowding events, and the SLA/congestion alert log. Positioned as a single curb-to-gate view, so the series span the full journey rather than one checkpoint, and dwell-time data is explicitly captured as a retail-revenue input

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Coverage

Universe, instruments and categories.

Industry: Airport ServicesApplication Software
Instruments: equities
Categories: Alternative

Dataset card

Type: not stated
Format: Tabular minute-resolution time-series per sensor zone / processing point (timestamp, count, wait minutes, queue length, dwell minutes, occupancy), with crowding and SLA-breach events as discrete rows; AI-derived from camera sensing, delivered through a cloud-native operations suite intended for system integration
Volume: Order 10^8-10^9 rows per year. Basis: the platform states minute-by-minute tracking, which caps out near 525,600 samples per zone per year; a mid-to-large terminal carries dozens to a hundred-plus sensor zones across check-in, security, immigration, boarding and retail areas, so each airport contributes roughly 10^7 reduced rows annually and the multi-airport estate compounds from there. Raw AI video-derived sensing is an order of magnitude larger in bytes. Archive accumulates year on year since alerting and SLA history require retention
Users: N/A — B2B airport-software deployment with no consumer account base. Scale should be counted per instrumented airport and per sensor zone; the platform's users are airport operations staff operating under operator licences, not registered consumers
History: 5 years
Update frequency: not stated
Growth: Active — roughly 110 staff on ~$12M revenue against ~$14.3M raised at Series A, a revenue-to-funding ratio that indicates a revenue-led company still investing rather than a stalled one. Marketing estate is freshly produced (current-year imagery and a recent customer video), a live customer reference sits inside a newly opened terminal, and the portfolio is broadening across airport operations, passenger processing and flow monitoring lines rather than holding at one product
Launched: Est. mid-to-late 2010s for the company; the Optic flow-monitoring line is a more recent addition. Basis: ~110 employees, ~$12M revenue and only ~$14.3M total raised at Series A is the profile of a firm that has traded for roughly a decade and grown on revenue rather than capital, and flow monitoring is presented as one line inside an established airport-operations and passenger-processing suite rather than the founding product. Flow-analytics products of this class commercially emerged in the early-to-mid 2020s, so treat Optic itself as newer than the company. No founding date is printed on the retrieved pages — confirm both dates with the vendor
Delivery: not stated
Entity mapping: not stated
Sample: not stated
Point-in-time: not stated
Licence: not stated

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