Data Foundation
Getting your data ready for AI is mostly unglamorous work: clean, trustworthy data; pipelines that keep running when nobody's watching; an architecture that can grow without a rebuild; and engineering decisions made with discipline, not under deadline pressure. Get this layer right and the rest of the organisation barely thinks about it. Get it wrong and every AI and analytics initiative inherits the problem.
A platform that holds up in production needs more than the right technology. It needs engineering discipline, operational rigour, and a clear view of how platform decisions today create or constrain what becomes possible tomorrow.
Modern Data & AI Stack
A modern data and AI stack is a set of choices about how data moves, where it's stored, how it's transformed and what you build on it. The wrong mix is fragile and expensive. We design and build a stack that fits your use cases, your team and your budget, on the platform you already run or the one that suits you best.
The right stack is the one your team can run.
Data Integration & DataOps
Data integration is where platform ambitions meet operational reality. Data lives in different systems, formats, and time zones, and getting it to flow reliably, consistently, and at the right latency is an engineering challenge that's routinely underestimated at the start of a programme.
DataOps brings software engineering practice to data delivery: version control, automated testing, continuous deployment and pipelines you can observe. The result is data that teams can trust, and a delivery process that doesn't require heroics to maintain.
Reliable data doesn't happen by accident. It's engineered.
Agentic Engineering Practice
AI agents are moving from proof of concept into production, and the engineering requirements are different from anything that came before. Agents need reliable data access, well-defined tool boundaries, orchestration logic, and governance frameworks that don't yet exist in most organisations.
Our Agentic Engineering Practice does this work. We help organisations design, build, and operate agentic data systems, from retrieval-augmented pipelines and autonomous workflows to the platform infrastructure that makes agents trustworthy and auditable in production environments.
Building agents that work in production is a different problem from building agents that work in a demo.
Data & AI Governance and Quality
Governance added after something goes wrong is much harder than governance built in. By the time data quality issues are surfacing in dashboards or AI outputs, the root cause is usually architectural: upstream processes that were never designed with consistency or accountability in mind.
We set up governance and quality that match your actual risk: who owns which data, how quality is measured, how lineage is tracked and how issues get fixed.
Data quality is a business ownership question first.
Capabilities
Everything included in a Data Foundation engagement
From platform architecture and governance through to the monitoring, quality checks and automation that keep it running.
Platform Architecture
Managed Services
Monitoring & Observability
Data Governance as a Service
Platform Build, Security, & IaC
Data Architecture & Semantic Modelling
Data Quality & Testing
Agentic & Platform Accelerators
What changes with a solid data foundation
Decisions you can trust: When data flows reliably through well-tested, observable pipelines, the outputs of analytics and AI systems carry a different kind of credibility. Teams stop spending time arguing about the numbers and start acting on them.
AI that's ready for production: Agentic systems and AI models are only as good as the data infrastructure beneath them. A platform built with AI in mind (clean data contracts, governed access, reliable pipelines) shortens the path from prototype to something the business can rely on.
Security that doesn't slow you down: Governance and security built into the architecture from the start creates a platform where compliance is the default, not a tax on productivity.
Operational confidence: Observability and monitoring mean problems get caught by the platform team, not reported by stakeholders, and the business stops finding out about data problems before the data team does.
A platform that evolves with you: Infrastructure as Code, modular architecture, and DataOps practices mean the platform can absorb change, new tools, new requirements, new scale, without requiring a rebuild every time the business moves.
Lower cost of ownership over time: In our experience, platforms built with cost observability from day one cost less to run.
Why work with us on your data foundation
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.
Accelerating Data Platform Migration for One of NZ's Largest Banks
Find out how one of NZ's largest banks partnered with us to help them accelerate their data platform migration with test automation.
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.
Other Services
Strategy & Advisory
Most stalled data and AI programmes skipped or rushed this foundational work to show progress elsewhere. Fixing that is usually cheaper than building on top of it.
Applied Intelligence
Applied Intelligence is where data infrastructure becomes organisational capability. It spans analytics and decision science, AI solution deployment, agentic workflow orchestration, and the AI architecture and governance that let organisations change how decisions get made.
Your questions answered
How do I get my data ready for AI?
Start with the use case, not the data. Pick one or two AI use cases and trace back what they need: which data, where it lives and who owns it. Then fix what those use cases depend on: definitions people agree on, pipelines that are tested and monitored, and access controls that decide what an AI model or agent can see. In our experience you don't need to clean everything before you start. Get the right slice right, then repeat. A Diagnostic is a quick way to see where you stand.
What does "data foundation" mean in practice?
It's the combination of data architecture, integration and pipelines, DataOps practices, and governance and security that everything else (analytics, AI, decision-making) depends on. When it's solid, it's invisible. When it's not, every downstream initiative takes longer and costs more.
Do you work with our existing cloud/data stack, or migrate us to something new?
Both, depending on what's there. If your existing stack is fit for purpose, we build on it. If it's genuinely constraining what you can do, we'll say so plainly and lay out what a migration would involve. We don't default to "rip and replace."
What is DataOps and why does it matter?
DataOps applies the discipline of software engineering (automated testing, version control, monitoring, and continuous delivery) to data pipelines. Without it, pipeline changes are risky and slow. With it, your team can ship changes confidently and catch data quality issues before they reach a dashboard or a model.
How do you handle data security and governance?
We build governance and security into the platform architecture itself. Access controls, data classification, audit trails, and compliance requirements are part of the build process rather than treated as a bolt-on step at the end.
General enquiries
Tell us what you're working on and one of our practitioners will get back to you within one business day.
