Bootstrap. Align. Shape. Evolve.

A short, fixed-timeframe engagement for cross-functional teams, run against either one of your own use cases or an external, learning-only one created by us. The structure adapts to you, but the goal is always the same: ensure participants gain real hands-on knowledge not just theoretical understanding.

A three week, part-time course for individuals who want to apply AI inside an enterprise specifically — not on a laptop. This is an individual course that focuses on more than just the basics of AI; it highlights the challenges that any AI champion will face inside an enterprise (and gives them tools to persuade others).

A time-boxed exercise to collect use cases from across your organisation, assess them technically, score them, and create a roadmap you can use to determine budgets, resources, and to set delivery expectations. We do this in a very interactive way, allowing your team to pitch and own their ideas, so once you decide what gets built, you already have the people that will own the products.

This is a short but powerful enagement where we assess the architecture of your AI use case and offer you potential areas of improvement. With the AI landscape constantly shifting, what is best practice today might not be best practice tomorrow - but this doesn't mean you should re-write your applications every month. We always try to balance the latest and greatest tech, with the cost and value it will bring.

A dedicated project to design and build an agentic platform that meets the needs (and constraints) of your organisation. We'll handle technical topics such as whether or not to use a model gateway or how to manage agent memory and data residency, as well as process topics such as establishing a RACI and a target operating model.

This assessment is designed around the requirements of the EU AI Act. We start by looking at whether or not your use case falls under the EU AI Act and, if so, we determine its risk classification. From there, we establish what requirements you have to meet and help you navigate these from a technology and process standpoint.

This is an individually scoped sprint for a single, well-defined use case. We usually tap into our library of reusable building blocks (if those can be applied to your business) rather than start from scratch. In addition, we usually require that your team is part of the building exercise because our goal is not to run these sprints for you forever, but rather create patterns you can reuse internally.

A project designed specifically to improve the user experience of your agents. With agentic applications, the user experience is no longer the difference between convering a potential customer or not. This new class of applications require an in-depth design of the user interaction to ensure token efficiency, prevent endless conversation loops, and protect your business from unwanted exploits.

Some situations require an industrialised, ongoing production line that can tap into a centralised library of building blocks, and apply these at scale. For example, we can help you build an application modernisation factory that uses AI agent swarms to tackle years of historical technical debt across your application estate, which would otherwise be unfeasible if done one by one.

This engagement handles one of the topics that all AI programmes will run into - how do we manage costs. Because AI use cases tend to have variable token consumptions, traditional budget & allocate FinOps strategies don't work. We help you look at costs holistically: forecasting, optimising consumption, limiting excess usage, and allocating costs even in multi-agent complex systems.

This sprint covers two topics that you've likely postponed, but that are absolutely critical in the long-term success of any AI use case. Proper evaluations (i.e., testing) of your AI applications is critical because they can help you optimise costs, conversation outcomes, as well as detect creeping drift inside your agents. A good security posture is one that you constantly maintain as the industry is learning more and more about how LLMs can be exploited.

This engagement is designed for very specific use cases where you need to train your own LLMs or deploy your own inference infrastructure on-premise or in a private cloud. For training, we usually suggest Amazon Nova Forge as a starting point. For inference, we can help you scope out your requirements and will stress test your assumptions since these will lead to a high-cost business decision. 99% of organisations will never need to even consider this.

If you use AI for the purposes of employment, the AI Act classifies your use as high-risk, and requires you to test the bias of your application. We help you audit whether your AI system performs un-biased decisions across all protected categories.

A strategy is only good if it's current. We offer ongoing advisory across your growing portfolio of AI applications: scaling what works, governing what is live, and keeping the assessment of both honest and aligned to your overall business goals.