What We Do
From machine learning infrastructure to polished end-user products, we cover the full spectrum of intelligent software development.
Custom AI pipelines, machine learning models, and intelligent automation built to solve real business problems at scale. This is the core of our practice — taking a problem that requires intelligent behaviour and engineering a system that solves it reliably in production.
We work across the full AI lifecycle: problem definition, data exploration and preparation, model selection and training, evaluation, deployment, and ongoing monitoring. We do not hand over a notebook — we hand over a production system with clear ownership, documentation, and monitoring in place.
AI system engagements typically run 2–6 months depending on data maturity and scope. We start with a 2–4 week discovery and prototyping phase before committing to a full build.
It depends on the problem. Some tasks (NLP classification using pre-trained models, anomaly detection) can work well with a few hundred labelled examples. Others (training custom vision models from scratch) require considerably more. We will assess your data situation in the discovery phase and design the right approach for what you have.
Almost everyone's data is messier than they think. Data cleaning, quality assessment, and pipeline engineering are a core part of what we do — not a prerequisite we expect you to solve before we can help.
Both, depending on what the problem requires. We always start by evaluating whether a pre-trained or fine-tuned model serves the need before investing in training from scratch. Custom training is appropriate when the domain is highly specialised or existing models cannot reach the required performance.
Intelligent applications for web and mobile platforms — fast, reliable, and designed with the end user in mind. We build the complete product: backend APIs, data layer, and user-facing interface, with AI capabilities integrated where they create real value.
The difference between an AI-powered application and a standard one is not just the model — it is the architecture that connects the model to the user experience reliably and at scale. We design applications where the intelligent component is a first-class part of the system, not an afterthought bolted on.
Web and mobile application projects typically run 2–5 months from scoping to launch. We work in short iterations and involve clients in regular reviews, so the direction can be adjusted as the product takes shape.
Yes, though we will want to do a technical review before committing to a timeline. Adding AI capabilities to an existing application is often straightforward; occasionally it requires architectural changes that need to be understood upfront.
We can arrange post-launch support packages for monitoring, incident response, and iterative development. The specifics are agreed on a project-by-project basis.
APIs, data tools, backend systems, and custom digital products that integrate seamlessly into your existing workflows. Not every problem requires a full AI system — sometimes the right answer is a well-designed data pipeline, a reliable integration layer, or a custom tool that eliminates a manual process.
We apply the same engineering rigour to these projects as to our AI work. The goal is always software that is reliable, maintainable, and genuinely useful in the hands of real users.
Yes, as part of a broader project. We do not offer standalone infrastructure management, but we routinely set up cloud infrastructure, CI/CD pipelines, and containerised deployments as part of application and AI system builds.
Not sure which service fits your needs? Describe your problem and we'll help you figure it out →