Our Approach

We think AI should be for everybody.

That’s why we don’t hide behind technical jargon, or try to make things sound more complicated than they need to be. The way we work is completely transparent, and we’ll bring you along for the ride.

It’s about collaborating, every step of the way. We’re not about ‘selling you a package’ you don’t want or need. If there’s a quick, simple, and cheap way to solve your problem, we’ll tell you.

We’ll show you our workings. We’ll share our insights. We’ll tell you exactly which technology and methods we’re using, so you can trust you’re getting the best solution. It’s flexible – like us.

How we work

The AI Blueprint

So much is possible with AI. Whatever the scope of your project, Fuzzy Labs use our AI Blueprint method to make sure your AI journey goes smoothly.


It’s a structured framework, which can flex to work for any sector, size or scale of AI project. So if you need AI to improve your marketing, medical tech, financial services business or anything else, you’re in good hands.

Our AI Blueprint approach is really straightforward, with three clear phases. There’s no minimum commitment – we’ll do as much or as little as you need. Here’s how we do it:

And throughout...

Keep getting smarter

1. Discover

Overview:

AI for your business starts with asking the right questions. We need to get to know what you’re about, so we’ll work with you to understand the problem in hand, the data available, and the vision for the future.


What happens:

We ask:
What do you have? What data is available, and how does this currently work within the business?

What's missing?
What technical or functional issues are currently lacking in the business?

What's the vision?
Refining the problem statement, and understanding the appetite for development.

What is possible?
Looking at AI solution options, challenges, initial hypotheses and anticipated benefits.


Outputs:

A document outlining the agreed problem statement, and the basis of the high level business case for AI development, and initial recommendations of possible AI implementations for the best result.

2. Prove

Overview:

Here’s where we build a quick and agile working AI prototype, to prove the AI application for the business. It won’t (usually) be the finished article. Think of it like a sketch – the outlines are there; the detail comes later. Where possible, we’ll lever existing cloud tech to create the minimum viable product (MVP) for the problem in hand. Or we might just take a pinch of your data, and model a custom machine learning application. Either way, it’s a light touch solution, which helps prove the business case for AI in your operation.


What happens:

  • Working iteratively, we use your data to build a working AI model
  • We'll experiment, using existing AI tools to quickly test the hypothesis
  • We build a working prototype, which can be tested for viability of the project
  • It shows what can be done, and how best to do it
  • Insights are gathered, stored and used to improve
  • We test, test again, improve and refine, building the working proof of concept to support the business case to stakeholders.

Outputs:

You’ll get a glimpse of how much more is possible for your business with the right AI. We’ll supply a working AI model – a validated and tested AI tool. It’s the basis of the roadmap for rolling out the AI solution as a fully operational solution in your business.

3. Do

Overview:

We’ve understood the problem. Built the outline sketch of how the solution should work. Here’s where we fine tune the AI application for a robust roll-out. We build a production ready solution and integrate it into your business, automating processes, detailing support structures and getting you set up for the ongoing success of the project.


What happens:

  • We take what we learned in the proof of concept, and scale it up
  • We create the live AI solution, integrating via API with your data and existing software
  • We refactor the code, reducing the risk of bugs or regressions
  • We build in automated testing and feedback loops for constant improvement
  • Working in sprints, any additional features and capabilities are developed – for data warehousing, machine learning, automation or ongoing support
  • Using best practice from software engineering, we employ CI/CD techniques throughout (continuous integration, continuous delivery), to make sure everything works seamlessly on roll out.

Outputs:

Every project is different. But with our flexible approach, the output will always be an AI application which does exactly what you need it to do. We can skill up and train your existing team to manage whatever we build for you, or work more collaboratively in a partnership longer term. The degree of ongoing support or development after that is up to you – but whatever the scale or scope of your AI project, we’ll take you all the way from idea to implementation.

Underneath it all

Constant Improvement

We’re always looking to make things better. It’s what AI applications do best.

Throughout every project, we work iteratively. We don’t just build solutions for the here and now - we work with one eye on what’s next, too. So you can trust that as well as solving the issue at hand, we’ll always be asking what else is possible at every stage of the way.