AI & Data agency in Sydney & Melbourne

Clean data is the foundation of any effective AI implementation. We audit your existing data landscape, identify quality issues and implement AI-driven routines that detect and correct inconsistencies in real time. Whether your data lives in an ERP, PIM, CRM or ecommerce platform, we build the data foundations your AI initiatives need to perform reliably. 

The quality of an AI system depends as much on how you communicate with it as on the model itself. We design, test and optimise prompt architectures and context engineering frameworks that ensure your AI tools produce accurate, relevant and commercially useful outputs, and keep producing them as your data and requirements evolve.

Large Language Models do not know your business, your products or your customers by default. We implement Retrieval-Augmented Generation (RAG) architectures that connect LLMs to your internal data sources, product catalogs, documentation, support knowledge bases, delivering accurate, up-to-date responses grounded in your specific context, with enterprise-grade data protection. 

Agentic AI goes beyond answering questions, it executes tasks. We design and build AI agents that integrate with your business applications via Model Context Protocol (MCP), automating multi-step workflows across your ecommerce platform, ERP, CRM and PIM. For B2B operations with high-volume, rule-driven processes, agentic AI can dramatically reduce manual effort and response times. 

No AI strategy is sustainable without governance. We help businesses define the frameworks, roles, policies and tooling required to ensure AI is used responsibly, securely and in compliance with emerging regulatory requirements. This covers data sovereignty, model auditability, team training, change management and the environmental footprint of AI infrastructure. 

AI delivers the most measurable commercial value in three areas for B2B and eCommerce businesses: revenue growth (product recommendations, personalised search, dynamic pricing), operational efficiency (automation of data enrichment, customer service, logistics workflows) and data quality (real-time detection of errors and inconsistencies across your product and customer data). The right starting point depends on where your biggest friction points are, which is why we always begin with an audit before recommending a solution. 

No. We design AI implementations that integrate with your existing technology stack, ecommerce platform, ERP, PIM, CRM, DAM, without requiring a full rebuild. AI is most effectively introduced as an intelligence layer on top of systems that are already working. The exception is when underlying data quality is too poor to support reliable AI outputs, in which case data foundation work comes first. 

It depends on scope and your current data maturity. A focused AI implementation, such as adding product recommendation logic to an existing ecommerce platform, or implementing AI-assisted content enrichment in a PIM, can typically be delivered in 4 to 10 weeks. More complex programmes involving LLM integration, RAG architecture or agentic workflow design run 3 to 6 months. We conduct a rapid audit at the start of every engagement to set a realistic timeline based on your actual starting point. 

AI automation applies machine learning or rule-based models to specific, well-defined tasks, classifying data, generating content, flagging anomalies. It is powerful but operates within fixed parameters. Agentic AI goes further: AI agents can reason across multiple steps, make decisions and execute actions across different systems autonomously. For example, an AI agent might receive a new supplier product feed, classify the products, enrich the attributes, flag missing data for human review and publish approved items to the ecommerce platform, without manual intervention at each step. 

Data sovereignty and security are built into our AI implementations from the architecture stage. For enterprise LLM and RAG projects, we design systems that keep your proprietary data within controlled environments, not exposed to third-party model training pipelines. We advise on hosting options (on-premise, private cloud, sovereign cloud) based on your security requirements, and our AI governance practice ensures the right policies and access controls are in place before any system goes live. 

Yes, and it is one of the highest-impact applications of AI for B2B businesses managing large catalogs. AI can generate product descriptions from structured attributes, classify new products into the correct category hierarchy, detect missing or inconsistent data across thousands of SKUs, and automatically tag digital assets based on visual recognition.

These capabilities are available within Akeneo and Pimcore, and Acidgreen configures and extends them to match your specific enrichment workflows and data governance requirements.