End-to-end imaging AI consultancy
Zegami’s end-to-end imaging AI consultancy services enable the healthcare, life sciences and manufacturing industries to deliver explainable imaging AI 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.
What challenges are you looking to overcome 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
- Quantify performance
- Lack of internal machine learning capability to deploy
- Need to continuously improve the machine learning model
How we work with you
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:
- Feasibility study – define outcomes of value and develop proof of concept to demonstrate efficacy
- Prototype – functional application proving viability to stakeholders
- Develop & deploy – move from prototype to minimum viable product, deployed in production
- Managed support – ongoing support, instrumentation and monitoring. Identifying signs of data drift and model retraining
- Certification – validation studies and AI explainability support
& train models
Develop & deploy