gliff.ai has launched its innovative software platform, specifically designed to assist the development of trustworthy Artificial Intelligence (AI) by addressing the gap for much-needed Machine Learning Operations (MLOps) products. MLOps is the emerging part of the technology world which makes developing AI easier for domain experts such as Doctors and Engineers to develop their own AI outside of the laboratory.
Starting as a Durham University spin-out in 2018, gliff.ai has already undertaken an interesting journey to date and received support from Northstar Ventures in late 2020 to accelerate product development.
gliff.ai has been developing its leading edge platform with the potential to shape the future of AI applications across industries, including healthcare and manufacturing. To achieve this, its team has grown from three to thirteen staff in less than a year. In addition, the company recently launched its new branding and website to celebrate its growth and prepare for an exciting future. gliff.ai is planning to grow quickly in the next 5 years, doubling revenue every year, and is just beginning a new investment round to fund growth.
 gliff has received investment and research grants totalling more than £800,000 to date. In November 2020, gliff.ai received £375,000 in seed investment from Northstar Ventures.
“The launch of our platform marks a significant milestone in gliff.ai’s journey, and through-out 2021 we will continue to develop our products and features,” explains Bill Shepherd, CEO of gliff.ai.
Today, AI already impacts our daily lives in many ways but we are facing a future in which AI will become even more integral to everyday life and business. The ability for this technology to accurately utilise image data is absolutely key for many applications, from medical diagnosis and quality assurance in manufacturing plants, to self-driving cars and anti-counterfeiting activities.
It is already well known that high quality data is essential for effective AI. Acquiring good data requires vast resources, using highly qualified experts with specific domain knowledge but little time. gliff.ai is creating tools to make this process of developing trusted AI as simple as possible.
gliff.ai’s platform, the first stage of its MLOps software suite will give users the functionality to:
- CURATE datasets, combining 1000’s of images and annotations, including image labels, all with complete dataset versioning
- ANNOTATE images with our intuitive tools to capture domain expert knowledge through image-level, region-level and pixel-level annotation
- MANAGE with teams on projects, including assigning images to individuals for annotation, compare annotations of different individuals and see progress with our project insights
These tools are now available to anyone – from academic researchers and data scientists, to blue chip companies and SMEs – who need to ensure their datasets are processed in line with the highest standards. To ensure users have all the support that they need, gliff.ai offers four plans tailored to best serve their different needs.
“gliff.ai wants to help businesses to be compliant with the emerging standards and frameworks for developing AI. Our tools also allow companies to audit their AI development process and work towards regulatory compliance,” says Chas Nelson, gliff.ai’s CTO.
gliff.ai’s ambition is to build an end-to-end toolchain of products that will permit domain experts, such as medical practitioners, to use their stores of data to create regulatable and trustworthy AI products. Obscured code used in the development of AI is unacceptable in regulated environments, which is why our code is Open Source and available for inspection by our users and regulators alike.
gliff.ai will also be releasing two further MLOps products in 2021, which will enable users to:
- REGISTER individual AI models and a complete training history in an auditable fashion with our AI model versioning
- AUDIT projects and access complete histories of datasets, annotations and AI models that can be downloaded to complete regulatory documentation.
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