Rapid prototyping and delivery of ideas in just 7 weeks.
Businesses invest millions in advanced analytics and data science projects. Unfortunately, the expected benefits often end up being at the end of the proverbial AI rainbow with few tangible outputs at the end.
Our ‘just seven weeks’ approach guarantees functional and usable products, prioritising tangible results over documents and worthless presentations. We believe machine learning alone is insufficient for unlocking value. To truly make a difference, it is essential to comprehend the business objectives behind a process and craft a solution that leverages deployable machine learning and AI technologies.
This process has worked so many times for our clients, we can guarantee functional, usable business outcomes in 49 days. We adopt a set of key principles to make this happen.
Our Just 7 Weeks Process
Understand
- Define the problem
- Agree solution
- Explore data availability
- Set mutual expectations
Prototype
- Rapid prototyping
- Known mathematical techniques
- No IT delay
Deploy
- Make available to users
- Does it work?
- How can we improve?
- Physical deliverables
Iterate
- Learn and build improvements
- Deploy more widely
- API
Why IT Works
We put time in a box...
We ensure success by deliberately time-boxing a project into seven weeks, agreeing deliverables, resourcing, data availability and platforms before kick-off, so once a project starts, there are no distractions.
...We guarantee usable outputs
We agree how you can use the results of the project in your organisation. This could be via Web Apps, APIs to connect to internal systems or coaching and handing over to your internal teams.
...We use small, and highly skilled teams
We keep our Labs teams deliberately small. This helps ease communication and ensures clear lines of responsibility with no overlap.
...With access to the world's best technologies
We have developed leading edge industrial computer vision models. As a certified Google Cloud Partner we have access to the most high-performance infrastructure for cloud computing, data analytics & machine learning.
...Working with clients in partnership
Success isn’t delivered in isolation. There will be demands on you to provide access to business users, knowledgeable employees as well as access to data and the support required to interpret it correctly. This will be defined and agreed during the initial stage.
Why do AI 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.