icetana (ASX:ICE) has received an expansion order to its self-learning artificial intelligence (AI) video analytics solutions to its existing Middle East-based customer technology reseller NIT, which operates several Middle Eastern shopping malls.
The company reports the expanded order will see an extra 600 cameras delivered to Tamdeen Mall Management and has a total value of US$138,000 inclusive of 12 months of support and maintenance.
The global software company notes the order represents a further expansion of icetana’s ‘strong’ presence in the Middle East retail mall surveillance marketing, adding to over 9,000 cameras currently using icetana in this key vertical.
“We are always pleased to secure long term expansion contracts with our existing customers”
Additionally, it is reported the contract is priced on an enterprise basis, meaning that future annual recurring revenue will be achieved from a support and maintenance arrangement likely to be in the range of 15% of the initial contract value (US$20,700).
The company also notes implementation will be undertaken by icetana technicians based in its Dubai office working closely with the end customer.
Material terms of the commercial arrangements include payment terms from NIT to icetana for these orders over a 45-day period. Further terms include icetana end user being subject to the End User Licence Agreement.
Speaking on the order expansion, icetana Chief Executive Officer Matt Macfarlane said: “We are always pleased to secure long term expansion contracts with our existing customers, this can only be achieved when our value proposition is strong and measurable. We are extremely pleased to continue working with a forward thinking organisation such as Tamdeen Group.”
icetana is a global software company which provides video analytics solutions designed to automatically identify anomalous actions in real-time for large-scale surveillance networks. The company’s software integrates with customers’ existing video management systems and IP cameras, and utilises AI and machine learning techniques in order to filter out routine activities, showing only unusual and interesting behaviour.
This technology allows operators to focus on events that matter and respond in real-time to both precursor activities and incidents.