Efficiency

Advancements in cameras used for computer vision, sensors to detect motion, pressure, sound, and atmospheric conditions, as well as ubiquitous high speed data networks have come together to deliver vast amounts of data, the Internet of Things, and Technologie 4.0. When applied to this data, machine learning algorithms can improve efficiency across industries. Process automation, real time detection, document processing, and predictive maintenance are a few of the areas where Aicadium customers are benefiting.

Process Automation

AI is supercharging process automation through the application of technologies such as computer vision and NLP to manage, automate, and integrate processes. It can be applied across almost any industry. Some typical use cases include quality inspection, predictive maintenance, rural surveillance, agriculture maintenance, trade execution, and claims processing.

View Case Studies

Document Processing

Natural Language Processing (NLP) has the potential to free up significant middle and back office resources for numerous industries to fundamentally redesign the structure and provision of services in light of disruptive innovations. Document processing is typically a 100% manual process however data extraction using NLP is transforming this into a largely automated operation. By identifying fields from contracts or forms across multiple different formats, AI engines can ‘understand’ the contents of documents and accurately extract information and insights, as well as categorise and organise documents. The main benefit is huge increases in labour efficiency – freeing up employee time for higher order tasks and being more responsive to clients.
View Case Studies

Real Time Detection

Many organisations, particularly within manufacturing or agriculture, rely on near real-time monitoring in order to ensure operations run smoothly and efficiently. Being able to detect deviations in standard operating procedures is critical and AI is providing organisations a way for data pipelines to ingest machine process data for near real-time intelligence.
View Case Studies

Predictive Maintenance

Having strong predictive maintenance tools in place enables businesses to anticipate when and where potential breakdowns in service can occur and move to respond to them in order to prevent potential interruptions in services. AI provides the ability to use volumes of data to anticipate, detect, and address potential issues before they lead to breakdowns in operations or services. AI-powered dashboards can display information, predictions, and parameter recommendations for operators. The solution relies on predictive maintenance models and defect detection models which enable operators to understand what needs to be addressed at the right time.

View Case Studies

Yield Optimisation

Yield Optimization or yield management has traditionally been defined as a group of strategies and tactics used to maximize the quality, and therefore potential monetisation, of goods or services produced.  In the manufacturing setting, this involves establishing quality standards of products coming off a production line and making sure the output adheres to those standards. In marketing, it involves maximizing the number of leads that convert to customers.  Aicadium applies machine learning to yield optimization to understand all relevant factors, enable the highest yields, and increase our customers’ bottom lines. 

View Case Studies

Case Studies

  • Efficiency

  • Industry

  • Search

See all

This site uses cookies to ensure you get the best experience on our website. Please visit our privacy policy page for more details.

Accept
Decline