From AI feasibility analysis through to software development, deployment and ongoing support of AI solutions, we've got it covered.
Why work with Filament?
Strong data science credentials
Our expert team, partnerships with leading technology providers and academic institutions, and a dedicated R&D function ensure that we remain at the cutting edge of AI developments.
Project management at our core
We pride ourselves on our strong client relationships, specialist AI consultancy and project delivery expertise. We have many years of experience at successfully delivering substantial global projects for clients.
Proven success
We understand there is much more to AI project delivery than just training a model. Our engineering expertise combined with the tooling we have developed helps us get you there faster.
At Filament, we help organisations understand what they can do with AI and we then help them deliver it - through our strategically lead engineering know-how and our software.
Our AI Transformation Launchpad
We have developed robust processes for combining conceptual business challenges with historical data to transform them into applied machine learning models that solve real problems for our customers.
Inception and Scoping
Most AI projects begin as an idea, often inspired by insights gleaned by our customers based on the patterns they see in their business. In this crucial early stage we work collaboratively with our customers to understand their hypothesis and to identify additional data in their business that could support it. We also discuss any functional software requirements for the prototype and our UX design team works together with the customer to design wireframes that lay out how the ML application will work.
Data Collection
Once an idea is scoped out, we work with the client to gather the relevant datasets to test their hypothesis. We employ a combination of data analysis best practices and software engineering know-how 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 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 if necessary. We provide a full report detailing the approaches taken and our recommended approach.
Application Development
Once we’ve chosen an effective model, we start 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 the concept we want to test is sound..
Handover & Education
Once the application is built, we hand over the application and the model to the customer. We then take the time to help them understand what we’ve built, the approach we’ve taken and provide recommended next steps.
Accelerating delivery with the Filament AI Suite
Our ‘home grown’ suite of tools solve many of the key challenges businesses typically face when developing AI solutions. We use these tools to help us deliver more efficiently for our clients.
We have solutions that help with processing data and training and rapidly deploying models – all trained by our data science team – 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.