Applied AI and data engineering
AI is becoming embedded across modern digital platforms, but value comes from how it’s applied and governed, not from the technology itself.
We help organisations apply AI and data engineering in practical, responsible ways, grounded in real data, clear intent, and the realities of operating complex platforms.
Why teams involve us in applied AI
Key benefits:
- AI grounded in platform and data reality — Applying AI with a clear understanding of the underlying platform, data quality, governance, and operational constraints.
- Engineering-led, not experimental — A focus on robust pipelines, reliability, maintainability, and cost management — not prototypes that never make it into production.
- Responsible and considered application — Helping teams understand where AI adds value, where it doesn’t, and how to apply it safely and transparently.
- Designed to support real teams — Solutions that fit existing workflows and capabilities, rather than introducing unnecessary complexity.
- Phased adoption and organisational readiness — Supporting education, change management, and phased rollout so AI capabilities can be adopted sustainably over time.
Where AI concentrates complexity
As AI becomes embedded into digital platforms, complexity increasingly concentrates in data pipelines, orchestration, and governance rather than visible features.
Early choices around data structure, integration, and operating models matter most in environments where content, search, and personalisation are all at play.
Our role is to help teams make those decisions with a clear view of the trade-offs and long-term implications.
Why Think Fresh Digital?
Applied AI with clarity
We apply the same standards to client work that we hold ourselves to internally, using AI to enhance delivery while keeping human judgement firmly in the loop.
- AI-first ways of working applied within our own delivery teams
- Strong grounding in data engineering and integration
- Experience applying AI within real platform and organisational constraints
- A pragmatic approach to risk, governance, adoption, and change
Our goal is to help organisations move forward with confidence, not uncertainty.
A considered approach to applied AI
Applied AI work varies by context, but typically follows a structured approach that prioritises clarity, learning, and sustainability.
Understand the problem
Clarify the business problem, data landscape, organisational context, and constraints before introducing AI.
Design foundations and readiness
Design data pipelines and integrations, while assessing organisational maturity and readiness for AI-enabled ways of working.
Apply SitecoreAI capabilities
Leverage SitecoreAI features such as agentic workflows, brand kits, assisted campaign creation, automation via MCP, and generative content.
Apply AI responsibly
Introduce additional AI capabilities where needed, with appropriate safeguards, governance, and long-term maintainability in mind.
From platform capability to applied value
Modern platforms like SitecoreAI provide many AI capabilities out of the box, reducing the need for custom development in many scenarios.
We focus on maximising return on those capabilities first, extending them where necessary, and only building bespoke AI solutions when a clear need exists.
- Applying SitecoreAI for agentic workflows and content operations
- Extending platform capabilities through Marketplace apps where appropriate
- Designing custom AI solutions when platform features alone are not sufficient
The focus is applied value, delivered responsibly.
AI delivers value when the agentic capabilities of platforms like SitecoreAI are applied strategically, grounded in context, and designed to support how teams actually work.