National Vehicle Intelligence Cloud
NAVIC™ is a South African company providing various tools and services in the provision of a security ecosystem for communities, business and government in the Security Industry.
Over 7.5 Billion since 2017
Total Plate Reads by Navic.Cloud
(01 Jan – 30 Sept 024)
(Over R61.8 Million)
Total Value of recovered goods YTD
(01 Jan – 30 Sept 2024)
NAVIC Approved Information Service Provider
NAVIC Alerts Forwarded to and Distributed by E2
(01 Jan – 30 Sept 2024)
What is NAVIC™?
The Navic.Cloud Ecosystem provides the necessary framework for the support of Security organisations togain and act on relevant vehicle related data in real-time so as to fight crime.
Navic.Cloud is the CORE system in order to affect the above. Navic.Cloud is a secure and stable system tying the numerous functions, users, data and services together in order to provide an early warning, intelligence based investigations platform. More specifically Navic.Cloud integrates vehicle related metadata from ANPR (Automatic Number Plate Recognition) cameras, analyses and processes the data, and cross references this with user compiled VoI (Vehicles of Interest) and 3rd party (like SAPS circulation / Unicode / ICB) databases. Manual verification is performed through NAVIC™’s Alert Room software solution and provides human escalation services as per instructions in the vehicles Incident diary as is standard in SAPS systems.
All this under the secure framework of the Independent Data Custodianship functions, and in line with Navic.Cloud’s Minimum Information Security Standards (NCMISS www.navic.cloud/ncmiss/)
Navic.Cloud is a business process driven system. It is therefore not unexpected to see that NAVIC™ is the only ANPR (Also known as LPR) provider to meet the following standards:
MISS Compliant
PSIRA Registered
Technology Choices
As in most technology based businesses NAVIC™’s technology choices are based first and foremost on the ever changing needs of a rapidly evolving market place. Thereby making sure that we remain relevant and able to meet the needs of this changing landscape.
The start of any project, and therefore choice of technology and implementation strategy, leverages the vast experience and knowledge we possess. This includes that of the Security Industry, Online systems, business rule sets, appropriate financial models, strict legal and security frame works, handling of Big Data structures, speed of operation, simplicity of user operation and human adoption of technology, our own chosen structures like NCMISS, and the provision of secure facilities for data based service offering to name the most important factors.
What we end up with is a well-structured Platform designed and implemented around stability and security, being user-centric, and able to be quickly supported and expanded upon.
NAVIC™ utilises the best cost efficient techniques and technology throughout Navic.Cloud and the associated Ecosystem so as to leverage the best innovations in the world today. And the ability to amend Navic.Cloud as this landscape changes over time.
This shift of approach has allowed NAVIC™ to move from old static algorithm based methodologies to a platform that will provide for multiple Machine Learning techniques and even proprietary built and billed systems.
Right now NAVIC™ applies a combination of techniques, such as pattern identification, Big Data Handling, dynamic vertical and planned horizontal resource allocation through VM’s, Hashed transaction logging, Immutable usage logs, distributed processing, encryption services, and many other best of breed techniques. All to provide a highly functional near-real-time data gathering, handling, processing, tracking, logging and alerting Platform which practically provides a warranted better than 99.9% service availability (T’s&C’s apply).
Navic.Cloud’s predominantly crime fighting ANPR Vehicle Analytics system is currently being expanded to provide a customer determined Process Flow to act on actionable events determined by a recognised behaviour from activity of objects identified by their signatures. This include pure Deep Learning Pattern Recognition for automated behavioural categorisation of behaviour, and tie this to concern levels.