Artificial Intelligence Consultancy for Imaging
Zegami’s imaging AI consultancy services enable the healthcare, life sciences and manufacturing industries to deliver imaging based AI solutions faster and more accurately. Our unique tools and machine learning (ML) services become your data science plug-in, enabling easy identification of patterns, outliers and trends in large and complex visual data sets.
Whatever challenges you have in your machine learning workflow, we’ll work with you to create, enhance and validate your ML models. We’ll help you to drive your business or project forward, maximising the value of your image data, saving time and helping to provide a quicker return on investment.
Leading AI consulting to overcome challenges with your imaging AI projects
& train models
- Lack of data science expertise & resource
- Require access to subject matter expertise
- Fast proof of concept requirement
- Managing & displaying large volumes of image data
- Time-consuming AI filtering and tagging processes
- Disparate data silos across organisation
- Designing a machine learning workflow
- Need to assess model explainability
- Achieve CE & FDA accreditation
- Quantify performance
- Lack of internal machine learning capability to deploy
- Need to continuously improve the machine learning model
Enabling cutting-edge vehicle damage detection
Partnership with Ebbon Intelligence to produce a platform that helps to adapt technologies for the fleet, rental, risk, compliance, and other related automotive sectors.
The partnership aims to enable car hire companies to accurately assess the depth and detail of damage to vehicles during rentals, benefiting both customers and rental firms.
Based on the industry standard DAVIS vehicle and driver risk platform, our platform will aid in the identification of changes in the condition of vehicles between hires for rental, movements for leasing and for potential applications to commercial vehicles.
Improving quality control to solve global food shortages
The Australian Plant Phenomics Facility work at such a large scale in the automation of growing and assessing plant varieties and species, that they needed support to manage the volume of image data.
The Zegami Machine Learning Suite delivered the support they needed.
A key component of their daily output is images of each plant from a variety of angles. These images must then be assessed by experts to determine which strains show the most promise. There is considerable scope for things to go wrong and so regular monitoring of the system is therefore essential to keep each experiment on track.
Accelerating treatment for Chronic Fatigue Syndrome
A collaboration with a medical researchers at Oxford University to try and find the cause of Myalgic Encephalomyelitis (ME), otherwise known as Chronic Fatigue Syndrome.
The project uses Peripheral Blood Mononuclear Cell (PBMC) data obtained from the UK ME/CFS biobank, which includes samples of ME/CFS, Multiple Sclerosis and healthy control groups.
A technique called Raman spectroscopy is then used and visualised in Zegami, which is able to clearly differentiate between the three different groups. These results represent an important first step in developing a diagnostic test for the bacteria.
A partnered approach to AI
We have many years of experience of working alongside researchers, data scientists and medical practitioners on a wide range of AI and machine learning (ML) projects that use imaging data. Over the years, we’ve developed a robust framework for collaborative projects, which can be tailored to the requirements of any project or context.
Through our partnership approach, you will be supported through any or all of the following stages of the AI & ML development process:
Define outcomes of value and develop proof of concept to demonstrate efficacy
Functional application proving viability to stakeholders
Develop & deploy
Move from prototype to minimum viable product, deployed in production
Ongoing support, instrumentation and monitoring. Identifying signs of data drift and model retraining
Validation studies and AI explainability support