Build the data foundations that make AI actually work.
With Wundr AI Lab.
A collaborative environment where your operational teams and our expert data engineers work side by side to build the data foundations that unlock AI's potential — equipping staff with new skills while augmenting them with Wundr's expertise in data engineering, governance, AI workflows, and automation.
Tangible outcomes AND hands-on capability growth — strengthening organisational readiness for data-led transformation.
✓ Learning to work alongside AI
✓ Designed for technical AND non-technical teams
✓ Real outcomes, not just training
What it unlocks
AI experimentation. Trusted data foundations. Team capability growth.
Experiment with AI confidently
Design safe, instrumented AI experiments that shorten the path from idea to insight — built on trusted data foundations.
Build AI‑ready infrastructure
Lay governance, quality, and access foundations so AI pilots plug into trusted data and scale responsibly.
Grow AI‑enabled teams
Upskill analysts and engineers in modern AI and data practices while delivering real outcomes, not just training.
Engagement model
3–6 month lab cycles: Discover → Model → Enable → Evolve
Discover
Map sources, flows, decision points, and outcomes. Establish governance guardrails.
Model
Shape business‑friendly schemas and genuine metrics people trust.
Enable
Prove value with targeted use cases. Ship real data pipelines and documentation as the team learns new skills.
Evolve
Observe and iterate. Transition into outcome‑focused phases that embed sustainable value.
Capabilities
Pipelines & Modelling
From source ingestion to curated layers; AI‑friendly patterns and observability.
Governance
Ownership, lineage, access, quality, and policy embedded in the workflow.
AI & Automation
Pragmatic pilots tied to your model that deliver measurable business outcomes.
Reference project
Evo Cycles
"We had the pleasure of working with Wundr AI Lab to optimise one of our key business processes, significantly reducing the time and manual effort required. The Wundr AI Lab team took a meticulous approach to understanding our workflow — a complex process involving the extraction of product data from supplier stock feeds, cleansing and mapping that data, and preparing it for import into our system.
Throughout the project, Wundr AI Lab collaborated closely with us to identify pain points and explore opportunities for optimisation. Their team demonstrated deep technical expertise and a genuine commitment to improving our operations. They were also patient and supportive, taking the time to explain technical concepts clearly to those of us without a programming background.
Thanks to the guys at Wundr AI Lab, we now have a more efficient process in place. We highly recommend them to any business looking to streamline and enhance their workflows."
— Evo Cycles, E-commerce retailer
Key focus areas: Process automation, data accuracy, operational efficiency, and knowledge transfer.
Indicative outcomes
- 94% categorisation accuracy – exceeding the 80% reduction target in manual effort.
- Cleaner, consistent data – creating a reliable foundation for reporting and decision-making.
- Faster results through collaboration – Evo's merchandising expertise + Wundr AI Lab innovation.
- Scalable approach – proven methodology ready to apply across other business areas.
- Future-ready platform – pipelines and frameworks designed for ongoing efficiency gains.
From the blog
Navigating the new knowledge landscape
How information shapes reality in the age of intelligent models — context for why governance, models, and meaning matter.
As intelligent models develop at an alarming rate, the lines between data, information, and knowledge are becoming increasingly blurred. What was once a clear hierarchy — data as raw facts, information as organised meaning, and knowledge as contextualised understanding — is now more fluid than ever before.
In his book Nexus, Yuval Noah Harari suggests that "information is not necessary to inform us" and "information is not necessarily a reflection of reality." Instead, he argues that "information puts things in formation." It doesn't merely reflect reality; it actively shapes how we understand it. In a world where intelligent models are not just processing data but also organising it into coherent narratives, the implications for organisations are profound.
If information shapes reality, then how organisations structure and manage information directly impacts how their people perceive problems, solutions, and opportunities.
Read the post
Why it matters
AI Lab practices are designed for this landscape: trustworthy data semantics, accountable ownership, and safe acceleration where AI adds real leverage.
Next step
Bring your data to life.
Let's start with a short conversation about your data landscape, your blockers and bottlenecks, the processes you want to accelerate, and the decisions you want to power.