Trusted to deliver AI capability for clients globally.

Our AI Transformation Launchpad

Filament has helped try and test AI and machine learning approaches in 100+ projects across more than 40 customers. We have developed robust processes for taking conceptual business problems combined with historical data and transforming them into applied machine learning models that solve real problems for our customers.


Supporting clients with AI initiatives

Download the factsheet (PDF)


Inception and Scoping

Most AI projects begin as an idea often inspired by insights gleaned by our customers based on patterns they see in their business. In this crucial early stage we work collaboratively with our customers to understand their hypothesis and to identify supporting data in their business that could support it. At this stage we also discuss any functional software requirements for the prototype and our UX design team work together with our customers to design wireframes that lay out how the ML application will work.


Data Collection

Once an idea is scoped out we work with our client to gather the relevant datasets to test their hypothesis. We employ a combination of data analysis best practice and software engineering know-how in order to store data in the right format. We have a number of complementary tools such as the Filament Smart Web Scraper as well as public and proprietary datasets at our disposal that can be used to bolster our clients’ use cases and strengthen the predictive power of the models we create.


Data Processing

Next we employ our applied statisticians, natural language processing experts or computer vision specialists to analyse and understand the datasets we have collected. We set to work to merge complementary datasets, remove bias and convert text and images into formats that machine learning models can understand.


Modelling & Evaluation

Having discussed our clients’ requirements, our machine learning specialists will train and evaluate a series of machine learning models using the prepared datasets. These models will be benchmarked against each other in order to identify models that work well for specific business problems. We start with simpler tried and tested machine learning models and iterate towards more complex power-hungry deep learning models only if necessary. We provide a full report detailing the approaches taken and our recommended approach


Application Development

Once we’ve chosen a model that we’re happy with we set to work building the business application around it. This is a group effort involving our software engineers, ML engineers, devops engineers and quality assurance specialists to ensure that the application we build proves that concept we set out to test is sound.


Handover & Education

Once the application is built, we hand over the application and model and spend some time with our customers helping them to understand what we’ve built, the approach we’ve taken and recommended next steps.

Accelerating delivery with the Filament AI Suite

We have built up a suite of tools that solve some of the key challenges when developing AI solutions. We use these tools to help us to deliver more efficiently for our clients.

We have tooling that helps with processing data, training models and the rapid deployment of models -that our data science team have trained – as scalable APIs, ready for use in real-world applications. We also have tooling that allows us to create complex workflows, pulling together a number of different machine learning models and integrating them with business line applications.


Why work with Filament?

Strong data science credentials

We have a full-time team, partnerships with leading technology providers and academic institutions, and a dedicated R&D function to ensure we remain at the cutting edge of AI developments

Digital agency approach

We pride ourselves on our strong client relationships and project delivery expertise – we have many years of experience within the team at successfully delivering substantial global projects for clients.

Proven success

We understand there is more to AI project delivery than just training a model. The business application and data must be understood. Models need to be deployed and used at scale, requiring engineering expertise as well as data science expertise. The tooling we have built up over our delivery of 100+  projects enables us to get there faster.

At Filament we understand the differences between an ML Engineer, an Applied Statistician and a Data Analyst and we can apply the right skills to your project as it progresses. Once we reach the end of the process, your existing in-house software engineers should be well placed to continue to maintain and improve your new models with minimal data scientist involvement.