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BESPOKE DATA VISUALISATIONS
AI ADOPTION SOLUTIONS
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IOT, CLOUD, DELIVERY & ENABLEMENT
KEEP YOUR SOFTWARE ROADMAP MOVING
AI-Pods bring experienced software engineers and an established way of working with AI to help you move important software priorities forward.
An AI Pod is a small team of experienced engineers engaged as a unit to deliver a defined area of software work. You set the backlog priorities, and the Pod organises its work around them through a shared delivery process.
Holisticon Connect’s Knowledge Layer brings together engineering practices and AI tools, while giving the team access to the project decisions and context you choose to share. This helps engineers and AI agents work with your project’s requirements and conventions. You can draw on this AI-supported delivery approach without first introducing an AI framework across your organisation.
A good starting point is a defined area of software work, with someone on your team to clarify priorities and accept the results.
Your roadmap needs more engineering capacity
Your team has important software work competing for limited capacity, and hiring experienced engineers takes time. An AI Pod can support a defined area of development while your team continues to guide product priorities.
Your specialists are focused on core systems
Your engineers are busy maintaining core products and systems, leaving less time for other software your organisation needs. An AI Pod can take on a separate area of work, with input from your specialists where needed.
You want to use AI in delivery with confidence
You see potential in AI-supported development, but want experienced engineers involved in guiding the work and reviewing its results. An AI Pod brings AI into an established engineering approach, with people responsible for the work.
A team ready to work with AI
An AI Pod brings together three senior engineers working across .NET, React and Python. They combine their engineering skills with AI tools and an established way of working.
AI grounded in your project
The team can work with project context you choose to share, such as your repositories, data model and conventions. This helps connect its engineering practices and tools to your project.
Experienced engineers guiding the work
AI supports development, while experienced engineers guide the process and review changes before they are merged.
Work your team can continue
The Pod delivers code with supporting tests and documentation. Each increment includes a handover to help your team understand and continue working with what has been built.
The right measures depend on your goals and the type of work. At the start of an engagement, you and the team can agree how to assess progress against a relevant baseline. Depending on the scope, this could include work agreed versus delivered, rework or defects found after release, and the effort needed to review AI-supported work.
Looking at these measures together helps put delivery results in context and keeps quality in view. Targets depend on the work being measured.
AI-Pods support defined full-stack software work, from developing the software for an MVP to extending an existing product. They can also help create internal tools and data and analytics applications, or build software that helps an R&D programme use a model beyond the notebook where it began.
In Pharma and Biotech, the focus is on non-GxP software around the science. This may include applications that help teams work with data from ELNs, LIMS or CRO files. Work requiring discovery, UX, DevOps or infrastructure needs to be covered separately.
Tell us what you’re looking to build and where your team needs support. We’ll discuss whether an AI Pod is a good fit.