AI & Agents
AI is only as good as the data behind it. We help you move beyond pilots and experiments, building AI that’s reliable, compliant, and ready to support your real business goals.
"You really need to structure your process before you automate it. You can't automate what you haven't documented."
Jeroen Visser
CTO at i-spark
Most companies are curious about AI but struggle to move beyond proofs-of-concept. “Which AI use cases actually create value for us?” “Do we have the right data and governance in place?”
The truth is: AI only works when it’s built on reliable and well-structured data. Without that, automation is fragile, models are biased, and ROI remains unclear.
Maybe you’ve tested a model that showed promise, only to find it impossible to scale. Or you’ve invested time and budget, but the return is still unclear. You deserve AI that works in practice, not just on paper.
We start with defining a clear goal.
A structured, dependable approach to AI that’s grounded in meaning and built for immediate use.
The structures behind your agent (prompts, flows, patterns) encourage predictable behaviour.
Content, metadata and embeddings are organised to ensure precise, relevant retrieval.
The logic behind the agent reflects how your teams actually work, supporting real tasks instead of generic examples.
Access controls, guardrails and monitoring keep interactions safe and controlled.
Definitions and foundational data guidelines that ensure consistency, trust, and quality across your data assets.
Insight into what needs to be strengthened or introduced, making sure your team is prepared for both current and future needs.
Your project is guided by senior i-spark specialists who bring structure, clarity, and depth. Work with a team with expertise in strategy, product leadership, and architecture.
You may collaborate with a Strategy Lead, a Product Owner, a Data Product Lead, Solution Architect. Our collective view ensures your strategy is both forward-looking and operationally realistic.
We strengthen this through our collaboration with recognised experts such as Frans Melenhorst and Clear Value, creating a partnership that enriches the outcomes of your project.
An AI Engineer designs and builds agents that work reliably with your data, tools and workflows. They shape the reasoning patterns behind the agent, structure prompts so responses stay consistent and design retrieval flows that help the AI use information correctly.
Their work focuses on behaviour as much as technology. They define boundaries, guardrails and decision rules that keep the agent safe, predictable and aligned with your goals. They also orchestrate tools, APIs and system interactions so the agent can take meaningful action where needed.
The AI Engineer creates intelligent systems that feel stable, clear and genuinely helpful in daily work. Their work develops an AI system that is testable, debuggable, adaptable and capable of supporting workflows with clarity and control.
Together we look at your ambitions, your strategic priorities, and the challenges you want to solve. You’ll gain clarity on which use cases are worth pursuing, what tools and skills you already have, and where the gaps are.
This phase gives you a realistic, transparent view of what AI can do for you today, without overengineering or chasing hype.
That means a clear business case and a realistic timeline. We design AI pipelines and models, which your team knows how they work, why they work, and how to keep them reliable over time.
You’ll understand not just the technical side, but also the risks, costs, and expected results; giving you confidence that AI is a trusted part of your operations.
Questions we often hear about AI & Agents.
No. You don’t need to hire a full AI department to get results. We provide specialised expertise and integrate with your team, so you can start small and scale when it makes sense, without overcommitting resources.
Messy, fragmented data is one of the biggest blockers we see. That’s why we start by assessing and strengthening your data foundations. With clean, well-structured data in place, AI becomes not only possible but reliable.
An AI agent is a system that can reason, retrieve information, use tools and support a workflow.
Unlike a chatbot, which mainly responds to text input, an agent is designed to perform tasks based on context, data and defined behaviour patterns. It can follow steps, interact with systems and support real work when designed properly.
RAG (Retrieval-Augmented Generation) helps an agent access the most relevant information at the right moment.
It improves accuracy, reduces hallucinations and ensures the agent uses real knowledge instead of guessing.
With strong retrieval design, AI responses become more grounded, consistent and aligned with your work.
Adoption is just as important as the technology. We design solutions that are explainable and easy to use, and we involve your stakeholders early on so that they feel ownership, not resistance.
Agents can help with a wide range of activities, including:
– answering knowledge questions
– summarising documents
– preparing reports
– supporting operational workflows
– analysing patterns
– assisting with quality checks
– generating content
– interacting with systems through tools or APIs
The actual scope depends on your data readiness and workflow design, but we can help with that!
We don’t just build a model and walk away. We set up monitoring, governance, and clear ownership so your AI continues to perform — and we stay available to support you as your needs evolve.
With i-spark, you get a partner who combines deep technical expertise with a pragmatic, ethical approach. We help you move past hype and into impact.