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Data and AI

Putting data and AI to work across your organisation.

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Specialist solutions
Overview

Data and AI is Avocado’s practice for turning data into measurable business outcomes. We work across three connected areas: data foundations that give your AI something trustworthy to work with, AI engineering that predicts, generates and acts, and adoption and governance that get your people using AI safely and confidently.

Our approach is technology-agnostic. We work across Azure, AWS, GCP, Databricks and Snowflake, and the current generation of large language models, including Claude, ChatGPT, Gemini, Grok and Copilot. Every engagement is built for your organisation rather than shoehorned into a template, and measured by the return it delivers, not just the technology deployed.

01 Data foundations The governed data layer every AI initiative builds on. 02 AI engineering Models and agents that predict, generate and act. 03 AI adoption and governance Teams fluent in AI, with the guardrails to use it safely.
Why does this matter?

Point solutions don’t compound. A connected approach does.

01

One question, multiple systems

Answering a single cross-system business question shouldn't require manually reconciling three or more systems that don't talk to each other.

02

Stalled GenAI pilots

Generative AI pilots stall when the underlying data isn't governed or trustworthy enough to put in front of customers, staff or regulators.

03

From answers to action

Competitors are moving from AI that answers questions to AI that completes tasks. Without a governed data foundation, you can't safely make that shift. 

04

Rising expectations

AI governance and security expectations from regulators, boards and customers are rising faster than most internal policies.

05

Value trapped in silos

 A chatbot here, a dashboard there. Each one solves a single problem, but none of them builds on the last, so value stays trapped instead of growing over time. 

Our approach

From data to autonomous decisions

Most organisations move through four levels of maturity: understanding what happened, understanding what’s happening now, predicting what’s next, and, where it’s safe and valuable, letting AI act autonomously on your behalf. Not every organisation needs to reach level four, and most shouldn’t attempt it without the earlier levels in place. A strong data foundation, such as a lakehouse, makes every level after it faster and cheaper to reach, which is why we assess where you actually are before recommending where to go.

Stage 1 of 4 Understand the past

A single source of truth

Governed data platforms, dashboards and reporting that tell you with confidence what happened.

How we work

How we deliver Data and AI projects

Most AI initiatives stall between pilot and production. Our staged approach proves value early, so you only make the big investment once the evidence is there.

Strategy and ideation

Define the problem, the expected uplift and the budget available.

AI Strategy Workshop
Investment5–10%

Readiness and feasibility

Understand your data, test the make-or-break assumptions, and confirm cost, feasibility and return.

AI Readiness & Feasibility
Investment5–10%

Proof of concept

Build a small, auditable model that shows the outcome can be achieved, without heavy pipeline work.

Investment10–20%

Implement, test & productionise

Production pipelines, drift monitoring, integration into the systems that act on the outputs, and MLOps to keep models performing.

Machine Learning
Investment60–80%

Typical share of investment. Ranges vary by project.

What's included in Data and AI?

Specialist solutions, one full service consultancy

01

Data foundations

The governed data layer every AI initiative builds on.

Data Lakehouse and Infrastructure

A governed, production-ready data foundation, deployed in as little as 5 days.

AI-Ready Data Layer

Define your business terms and metrics once, so every AI tool and report gives consistent, trusted answers.

Data Migration and Data Cleansing

Move data out of spreadsheets, legacy systems and old warehouses, cleaned and fixed before it lands in its new home.

Reporting, Analytics and Insights

Reporting and single sources of truth that show what's happening across the business.

Data Governance and Security

Access, quality, lineage and privacy controls that keep your data trusted and compliant, whether it feeds a report or an AI model.

02

AI engineering

Models and agents that predict, generate and act.

Machine Learning

Predictive analytics that tell you what's likely to happen next, and why.

Generative AI and LLMs

Content, chat and reasoning, tuned to your business.

Agentic AI

AI that acts on your behalf, not just answers.

Machine Vision and IoT

Video, images and signals turned into automated decisions.

AIOps

Put AI to work on your IT operations, with faster detection, root cause analysis and remediation.

AIOps
03

AI adoption and governance

Teams fluent in AI, with the guardrails to use it safely.

Generative AI Training

Microsoft Copilot, ChatGPT and Claude training for individuals and teams. AI upskilling that sticks.

AI Governance Framework

Aligned to ISO/IEC 42001, NIST AI RMF and Australia's Guidance for AI Adoption.

AI Testing and Validation

Testing that AI models and agents give accurate, consistent and safe outputs before go-live, and keep doing so as they change.

Quality Engineering

AI Security

Protection against AI-specific threats, including prompt injection, jailbreaks, data leakage through AI outputs and unauthorised agent actions.

Cyber and Operational Resilience

AI Observability

Monitoring of performance, cost, accuracy and agent behaviour once AI is live, so issues are caught before they reach customers.

Observability
Not sure where to start?

Begin with an AI Strategy Workshop

A 1–3 day workshop that maps your opportunities and builds a prioritised roadmap before you commit to a strategy, budget and plan. Already know the idea, just not whether it stacks up? See the AI Readiness and Feasibility Study.

Explore AI Strategy Workshop
Why choose Avocado?

Deep technical specialists who speak the language of the business

Deep specialisation across data, AI, ML and IoT — paired with the business and communication skills to translate it into outcomes executives actually care about.

Technology-agnostic by design — Azure, AWS, Google Cloud, Databricks and Snowflake, and the current generation of LLMs including Claude, ChatGPT, Gemini, Grok and Copilot.

Every recommendation weighed against the return it delivers, not just the technology behind it.

Custom-built for your organisation rather than shoehorned into a template.

A staged path from strategy to production, so you prove value before the big investment.

Governance built in — aligned to ISO 42001, NIST AI RMF and Australia's Guidance for AI Adoption.

Technology partners we work with

5-day
Lakehouse build
governed, production-ready foundation
75-1
Enterprise AI

A governed, in-house alternative to Copilot

An economics consultancy wanted AI productivity without exposing client data. We built a secure, custom AI platform with governance and the firm’s workflows built in.

Read the case study
Due diligence
Data Modernisation

From Access database to cloud lakehouse

An Australian manufacturer had outgrown its legacy Access database. We moved its data into a cloud lakehouse with front-end apps and system integrations, creating one reliable source of truth.

Read the case study
4
connected stages
past, present, predict, act
ISO 42001
governance aligned
NIST AI RMF + AU guidance
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Agentic AI

From answers to action

Where it's safe and valuable, agents complete multi-step tasks using real tools — not just produce answers.

Read the case study

Common questions

What does "Data and AI" cover at Avocado?

Everything from foundational data platforms and governance through to predictive machine learning, generative AI, and autonomous agentic AI — treated as one connected practice rather than separate technology silos.

Can we bring in a Data or AI specialist to work inside our own team?

Yes. If you need capacity or capability inside your own team rather than a fixed-scope engagement, our Project Delivery covers data engineers, data scientists, machine learning engineers, forward deployed engineers and other Data and AI specialists on a resource-augmentation basis.

Do we need a data platform before we can do AI?

AI is only as reliable as the data underneath it — a governed Lakehouse, and for AI specifically an AI-ready data layer, is a strong foundation. Smaller point solutions can take a less structured approach to reach a result quickly, while keeping the bigger picture in mind.

What's the difference between generative AI and agentic AI?

Generative AI produces an answer or a piece of content in response to a prompt. Agentic AI plans and completes a multi-step task using real tools, adapting as it goes. See the Agentic AI page for the full comparison.

How long does a Data and AI engagement typically take?

It depends on scope — a Lakehouse foundation is a fixed 5-day build, while strategy, governance and agentic engagements are scoped individually. Most organisations start with a short workshop before committing to a build.

Where should we start if we're not sure what we need?

The AI Strategy Workshop is the standard entry point: a short workshop that maps your opportunities and gives you a prioritised roadmap before you commit to any specific build.

Related resources

Keep exploring

Blog

Why your AI needs a data lakehouse

How a governed data foundation makes every AI initiative faster and cheaper.
Blog

Agentic AI vs Generative AI

What's the difference, and why does it matter?
Advisory

AI Readiness and Feasibility Study

Know whether you're ready, and whether it will work, before you commit.
Advisory

AI Leadership and Governance Workshop

Get your leaders aligned and your governance in place.
Let's talk

Ready to talk about your Data and AI strategy?

Book a consultation, or start with an AI Strategy Workshop to map your opportunities first.

Book a consultation