AI consulting that reaches production
Applied Intelligence
Applied Intelligence is Data Domain's AI practice. We design, build and govern AI solutions, agents, workflow automation and decision science that New Zealand organisations run in production, with monitoring, evaluation and a named owner for each model.
Most organisations have dashboards. Fewer have AI that puts the right answer in front of the right person, and fewer still have the engineering to keep it reliable as it changes.
AI Solution Development
Most AI solutions don't fail because the model was wrong. They fail because the solution wasn't designed around how people work, what data was realistically available, or what would need to change for the output to be trusted and acted on.
AI Solution Development covers the full arc from problem definition to production deployment: use case scoping, data readiness, model selection, integration design, and the testing and evaluation that shows whether it's ready to run at scale. We build for production and for the team that has to maintain it.
If you can't measure or explain an AI solution, you can't run it.
Workflow and Process Automation
Organisations that successfully scale AI beyond isolated pilots do so because they treated AI as an operational capability, with the same engineering discipline, governance, change management and workforce readiness they'd apply to any critical business system.
We help organisations identify, redesign and automate the workflows where AI makes the most difference, moving from individual AI experiments to AI in daily operations. We design the platform patterns, governance and team structures so AI use cases can be repeated and measured across the business.
One AI use case is a pilot. Ten is an operating model problem.
Decision Science
A dashboard nobody checks stops being an analytics asset and becomes a maintenance overhead. The difference between business intelligence people trust and act on, and one they ignore, is rarely the tool. It's whether the metrics mean something to the people looking at them, whether the data behind them is reliable, and whether the design respects how people make decisions.
We create analytics products across different toolsets and apply statistical reasoning, simulation, and optimisation techniques to the business decisions where better analytical rigour produces measurably better outcomes.
Better decisions come from understanding how the data links to outcomes.
AI Architecture and Model Governance
Early AI architecture choices limit everything that follows. Provider lock-in, brittle integration patterns, and models deployed without evaluation frameworks create technical debt that's expensive to unwind, and governance gaps that create risk the business may not yet be aware of.
We design AI architecture that is provider-agnostic, maintainable across model generations, and governed for the risks that show up in production: bias, drift, explainability and accountability.
A model deployed without governance is a risk you haven't disclosed.
Capabilities
Everything included in an Applied Intelligence engagement
From the first process map through to a model in production, governed, monitored and built on infrastructure you can move off at any time.
Process Mapping & Optimisation
Workflow Integration
Predictive Modelling
Responsible AI Controls
Management Dashboards
RAG, LLMs & Agentic
Provider-Agnostic Architecture
MLOps, Evals & Observability
What does Applied Intelligence actually change for an organisation?
Decisions grounded in evidence, not instinct: Decision science and predictive modelling shift the question from "what do we think will happen?" to "what does the evidence suggest, and how confident should we be?" That shift changes how resources are allocated, risks are managed, and opportunities are prioritised.
AI in production: Agentic deployment and scaling AI for operations are what turn a set of proofs of concept into something the business uses every day.
AI you can keep trusting: MLOps, evals, and observability mean that model performance is actively monitored rather than assumed. When something changes, and it will, you find out through your monitoring infrastructure, not through a stakeholder complaint.
Accountability built in, not retrofitted: Responsible AI controls and model governance frameworks mean that when regulators, boards, or customers ask how a decision was made, there's an answer, not a gap.
Faster, more reliable delivery through automation: Workflow and process automation reduces the manual effort that sits between insight and action, so people spend their time on the judgement calls.
Architecture that doesn't lock you in: Provider-agnostic design and model governance mean you can change model providers as the market changes, instead of staying tied to your first vendor decision.
Why choose us for your Applied Intelligence layer?
Implementing Generate's Greenfield Data and AI Platform
How we built Generate a greenfield data and AI platform on Snowflake and AWS, taking reporting lead time from days to zero.
Working with One NZ to Enable Customer Insights
How we built One NZ a single view of its enterprise customers, cutting account research from about 20 minutes to under 5.
Applying AI in the Enterprise
Explore how Applied Intelligence turns AI prototypes into production-ready systems through process mapping, governed architecture and staged rollout.
Other Services
Strategy & Advisory
Applied AI initiatives that stall after the pilot are usually held back by a strategy that was never tied to delivery, data maturity nobody checked, or a platform nobody assessed for production AI.
Data Foundation
An AI agent or model is only ever as good as what it's reading from underneath: the quality of the data feeding it, the reliability of the pipelines delivering that data, the soundness of the architecture it runs on, and the engineering rigour behind all of it. Skip that groundwork and even the most capable model will produce answers nobody can trust.
Your questions answered
What's the difference between Applied Intelligence and generic "AI consulting"?
Applied Intelligence means the AI gets into production and stays there, with monitoring, governance and a clear owner. A lot of AI consulting stops at the proof of concept. We do the less glamorous work of keeping it running.
Do you build custom AI models, or use off-the-shelf tools?
Both. We start from the business problem, not a predetermined tool. Sometimes an off-the-shelf model or platform capability (like Cortex AI or Fabric IQ) is the right fit. Sometimes the problem needs custom model development. We're technology-agnostic on this by design.
What does "responsible AI" mean in this context?
It means AI systems that are explainable, monitored for drift and bias, and governed by clear ownership and escalation paths. So when an AI system makes a recommendation, someone can explain why, and there's a human accountable for the outcome.
What needs to be in place before we start an AI project?
A use case with a business owner, access to the data it depends on, and agreement on how you'll judge whether it works. If the data underneath is unreliable or ungoverned, we'll tell you, and fix that slice before building on it. A Diagnostic is a quick way to check where you stand.
Do you build with LLMs and AI agents?
Yes. We build RAG, LLM and agentic solutions with evals and observability, so you can see how they perform once they're live. We choose the model provider to suit the use case and design the architecture so you can change providers later.
Can you automate workflows without a full platform rebuild?
Often, yes. Agentic workflow automation can frequently be layered onto an existing data foundation rather than requiring a ground-up rebuild, though if the underlying data is unreliable or ungoverned, that usually surfaces quickly once you try to automate on top of it.
General enquiries
Tell us what you're working on and one of our practitioners will get back to you within one business day.
