SUMMARY

Almo Intellect is a tech company applying computer vision and artificial intelligence to urban mobility, traffic management and road safety. Almo develops VISU, a mobility intelligence platform combining proprietary detection models, edge and cloud processing and a digital twin of the road network. VISUCloud, a SaaS used by municipalities and transport agencies. VISU Live feeds that data into a digital twin, so planners can spot patterns, predict situations and test scenarios before acting on the street. Almo is a member of NVIDIA Inception and Microsoft for Startups, has processed millions of vehicles. The result: continuous, network-wide mobility intelligence at a fraction of the cost of traditional studies.

PROBLEM

Cities still make mobility decisions with data that is scarce, expensive and quickly outdated. Traffic counts are done in wa way that over a few days at isolated points; consultancy studies take months and describe a network that has already changed; dedicated sensors cost too much to deploy widely. Desktop simulation tools that could close the gap depend on specialists and static inputs, out of reach for most planning teams. As a result, municipalities, transport operators and road agencies cannot see how their network really behaves, cannot measure the effect of their measures, and cannot test alternatives before committing budgets. In Europe this gap is now also a compliance issue: under the revised TEN-T Regulation, more than 400 urban nodes must adopt Sustainable Urban Mobility Plans by 2027, with monitoring obligations that few mid-sized cities are equipped to meet. In emerging cities, the same gap holds back road safety and transport reform.

SOLUTION

VISU works in three layers. First, sensing: proprietary computer vision models (detection, multi-object tracking and licence plate recognition), trained on real traffic footage and optimised for NVIDIA edge hardware, process video from Almo's VISU sensor units, drones or a city's fixed cameras. Second, data: VISU Cloud turns that video into structured mobility data (vehicle and pedestrian counts by class, origin-destination matrices, speeds, queues, conflicts and near-misses) and generates traffic engineering reports automatically, replacing weeks of manual work. Third, decision: VISU Live feeds the live network state into a lane-accurate digital twin with a scenario engine, so planners can detect patterns, forecast situations with Almo's prediction models and test closures, signal timings or bus lanes before applying them on the street. A companion product, VISU Road Eval, uses vehicle-mounted cameras to automate iRAP road safety coding.
The impact is measurable. Cities obtain continuous, network-wide data at a fraction of the cost of traditional surveys and can evaluate measures within days, not after the next study. Transport operators gain the evidence to redesign routes and terminals. Road agencies get safety audits at a scale manual coding cannot reach.