Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. The term AI is often used as an umbrella term for machines that are capable of perception, logic, reasoning, and learning. Another useful way to think about AI is in terms of a spectrum from automation (rules-based) to intelligence (learning systems) applied to various problems or use cases. Machines are good at repeating processes and producing predictable outcomes. What machine learning provides is the identification of patterns and anomalies; and then scaling, repeating and producing consistent results. AI enables optimisation and intelligent automation to augment human ability. Systems can now be built that perform functions way beyond the limits of current human ability.
We have a strong capability in Artificial Intelligence and Automation and apply these capabilities to mainstreaming AI, strategy and solution development and integration.
We strive to be platform and vendor agnostic
We form part of an eco-system of Altron and Partner capabilities that enables provision of end-to-end offerings
We focus on complex problems that are not readily solved with off-the-shelf systems
Ethical application of Artificial Intelligence
Why choose Altron Systems Integration?
reduce the risk of adopting AI through concrete showcases and a measured roadmap to enterprise implementation
robust and scalable solutions from concept to integrated solution
is to integrate AI and continuously improve our clients’ business
on AI and AA working towards mainstreaming AI
By combining process automation, machine learning and natural language processing to routine activities it mimics, and augments tasks performed by humans and over time it learns to do them better.
This drastically improves efficiency and performance, reduce operational risk and frees time of humans to focus on higher value activities.
User demand, supplier orders, and inventory levels are all improved using machine learning and deep learning systems. Demand is more accurately predicted while reinforcement learning can act on these predictions.
This improves inventory teams’ efficiency resulting in cost savings in terms of unnecessary stock to be stored and reduced waste due to stock expiration.
Mobile networks are becoming ever more complex, subscriber demands are increasing (reliability, coverage, bandwidth, security, etc…) and immediate action is often required.
Deep reinforcement learning AI on a combination of traffic, network and customer data, achieves a greater balance between network economics, performance, and end-user experience.