We use AI to augment your process in just 7 weeks, and we guarantee functional, usable business outcomes, not a set of technical deliverables.
Your AI Solution
The ability to create deep learning models is not enough to unlock value in your organisation. To really make a difference you must understand the business objectives behind a process and then craft a solution that delivers them through the use of AI and Machine Learning.
We work with you to understand how a process can be improved through ML, develop models, and then a method for embedding them in the process using web apps or APIs.
Within 7 weeks, our approach guarantees functional, usable business outcomes rather than a set of technical deliverables which may not deliver the intended benefits.
Success is not delivered in isolation, however. There will be demands on you to provide access to users, knowledgable employees and to data, and the support required to interpret it correctly. This will be defined and agreed during the initial stages.
Why most AI & ML projects fail
Objectives and scope are too loosely defined creating ambiguities around robustness and success.
Following a waterfall model rather than an iterative approach. A waterfall model can work for IT projects but is much less effective with data science.
Not recognising that advanced analytics is often a creative process, better suited to small, highly skilled teams rather than large consultancies.
Internal IS policies making it difficult to explore and deploy technologies and platforms quickly enough in order to meet the requirements of the project.
Seeking to shoe-horn solutions into proprietary platforms which look great but need considerable re-engineering to actually solve the problem.
Understand & Create
Define the problem
Agree on a solution
Explore data availability
Set mutual expectations
Known mathematical techniques
No IT delay
Business Outcomes in 7 Weeks. Guaranteed.
At Keen AI, we are different. We deliver functional, usable business outcomes rather than a set of technical deliverables. We can achieve this because: