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/Index/Synapsi (Synapsi.ai)/Synapsi (Synapsi.ai)
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Synapsi (Synapsi.ai)

SYNAPSI (SYNAPSI.AI)Manage supplier listing

Camera-derived transport-hub analytics generated from clients' pre-existing CCTV estate, so the same capability applies to both airports and rail stations: live crowd density and occupancy per zone against safe-capacity thresholds, directional passenger flow and path data across concourses, corridors and interchanges including counter-flows, queue length and wait estimates at gates, ticketing and security, platform and track safety events, concession and advertising-space footfall and dwell, and a searchable labelled incident archive covering falls, fights, abandoned objects and rough sleeping. Because it runs on installed cameras rather than new sensors, the dataset inherits the full camera density of an existing hub, which is broader coverage than a new-build LiDAR deployment typically achieves

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Synapsi (Synapsi.ai)/Synapsi (Synapsi.ai)
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Coverage

Universe, instruments and categories.

Industry
Application SoftwareRail Transportation
Instruments
equities
Categories
Alternative
Geospatial
Sentiment
Regions
USUKOther
Sample tickers
FSM.MIIAG.LBAL.LDALUALAALRATP.PA
Dataset card
Type
not stated
Format
Structured metric and event streams served over API with standard integrations into control-room tooling, VMS and PSIM: high-frequency per-zone time-series (occupancy, density, directional flow, queue length, wait minutes, dwell) plus discrete timestamped incident records with type, location and confidence, retained in a searchable event archive. Latency is specified as under 2 seconds from camera event to control-room alert, so the primary form is a real-time stream with the archive as the accumulated secondary asset
Volume
Potentially high per site and low in aggregate. Per instrumented hub, order 10^7-10^8 metric rows and 10^5-10^6 archived event records annually, because a live CCTV estate typically runs tens to hundreds of cameras and the product derives metrics from all of them rather than a sparse new sensor grid — the reuse-existing-cameras design is precisely what makes per-site volume high. Aggregate volume across the vendor is however bounded by customer count, which a 2-person company plausibly holds in single digits, so total licensable volume is modest today
Users
N/A — B2B control-room deployment with no consumer accounts; scale is counted per instrumented hub and per camera or zone, not per user. There is no registered population, and by design the product does not identify individuals
History
2 years
Update frequency
not stated
Growth
Active but effectively pre-scale — a 2-person founding team with no funding on record. Activity evidence is real and technical rather than promotional: quantified production accuracy of 98.4%, a sub-2-second latency specification, per-camera threshold and restricted-zone configuration, integration support across multiple named control-room and VMS/PSIM ecosystems, and a live demo, all of which indicate working product deployed somewhere rather than a pitch deck. Scale evidence is absent: no headcount beyond two founders, no disclosed funding, no named customer, and multi-sector marketing across transport and events that reads as a young company searching for a wedge market
Launched
Est. 2023-2026 — recent vintage. Basis: a 2-person company with no funding record and an organisational profile that has only just appeared in corporate databases is a strong recency signal, and the product's positioning on top of recent commodity computer-vision capability fits the same window. The company's own reference to validated production datasets shows the model has been trained and run somewhere, so it is not day-one, but there is no sign of multi-year market presence. No founding date appears on the retrieved page; treat this as a bounded estimate and confirm 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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Synapsi (Synapsi.ai) has not added their own details yet. Not yet on file:

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