We’re building an AI-powered operating system for visual asset management.
We survey assets using the most appropriate method; Helicopter, UAV or BVLoS.
Experts assisted by AI undertakes fittings assessment, defect detection and steelwork corrosion analysis.
Risk models predicts the future state of steelwork and the risk of fitting failure using FMEA and environmental data.
Generate investments plans by combining current and future state with financial and operational contraints
Our KAI platform uses deep learning to extract objects of interest and data engineering to ensure this visual data is easily available to anyone who needs it.
The KAI platform extracts key components from footage presenting them for review by a condition assessment engineer. Our models aim to extract over 50% of defects automatically.
Using KAI reduces assessment time by 66% and minimises unforeseen asset failure.
Bespoke edge detection algorithms are used to identify regions of the image likely to contain steel and thereby separate background from tower.
Eliminated 25% of towers from the manual review process by marking them as clean.
Digitising networks requires an inventory of what’s installed and where. This could be done manually by reviewing historic images and video. With thousands of structures and millions of images this is an exhausting manual process but an ideal opportunity for digitalisation and a practical use case for AI.
We are working with customers to complete detailed asset inventories using millions of images.
We have developed a vehicle mounted solution for collecting high resolution imagery of roadside vegetation. AI is then used for detecting the presence of invasive plant species or Ash trees.
This provides a rapid, high-quality vegetation survey methodology, resulting in cost and time savings for our customers.
Knowing the types of habitat and plants alongside rail tracks is crucial for Network Rail.
We have deployed camera systems to the front of trains for collecting images. The KAI platform analyses data gathered and presents an overview to NR staff.
Amjad Karim reviews some of the important lessons we learned whilst implementing the KAI platform at the UK’s National Grid.
Britain’s 20,000-mile rail network requires reliable monitoring of lineside vegetation. Together with the UK Centre
Press Release, 26.05.2022 Trials in using artificial intelligence (AI) to identify species of trees and
Over the coming months, Keen AI is working with National Grid and sees.ai developing a
Knowing what assets are installed where is important for an asset manager. Single towers can
In 1992, Polish forests were facing unprecedented levels of death amongst European ash. Ecologists were
At the start of 2020 we didn’t know if it would be possible to collect
At Keen AI we create systems which augment process experts, for example ecologists or engineers,
As the majority of us look to 2021 and hope for a better year, it’s easy to look back at 2020 and only remember what went wrong.
We digitise networks of assets, helping you unlock value from visual data.