AI Services Company
so the manual work stops being yours.

We build practical AI that runs inside your business systems. AI agents that handle real tasks, RAG knowledge assistants that answer from your own documents, chatbots and voice bots, AI document and invoice processing, machine learning models, and AI-driven workflow automation. Every build is custom-scoped, wired into the tools you already use (Xero, MYOB, HubSpot, Salesforce, SharePoint) and shipped on a production stack. Not demos, not proofs of concept that sit on a shelf.

Every project is fixed-price scoped after a free 15-minute call. No hourly billing. A focused AI proof of concept is smaller work. A full production build with system integration and machine learning is larger and depends on data volume, model complexity and how many systems it touches. Cloud and model-provider costs (OpenAI, Anthropic, AWS Bedrock) are billed at cost, separately from the build.

About 4 to 6 weeks for a focused proof of concept. 8 to 16 weeks for a full production AI build depending on complexity. We deliver in phases so you see working results early and can adjust direction based on real outcomes. First working AI output is usually in your hands inside the first fortnight.

Yes. Most of what we do is add AI features to systems you already run. If your software has an API or we can reach the database, we can wire AI into it. CRM (HubSpot, Salesforce, Pipedrive), ERP, accounting (Xero, MYOB, NetSuite), SharePoint, Teams, Slack, Outlook. We also build fresh systems with AI features from day one when that's the better fit.

Yes. Where possible, we deploy AI inside your own AWS or Azure tenancy so your data never leaves your network. When public cloud AI is used, we configure enterprise security (encrypted connections, access controls, data processing agreements) and never send your data to train public models. Regulated industries are our normal client base, so this is the default posture, not an upgrade.

It depends on the type of AI. RAG systems and AI agents work with whatever documents and data you already have. Machine learning models typically need more structured historical data. During the strategy and readiness stage we'll tell you honestly whether your data is ready and what prep it needs. No wasted budget on projects that were never going to work.

A chatbot follows a conversation flow and answers questions. An AI agent goes further, it can research, reason, take actions and complete multi-step tasks. For example, an operations agent can check your CRM, look up an invoice in Xero and send the customer a payment link. A chatbot would tell the customer to call accounts. We usually pair both.

RAG stands for Retrieval-Augmented Generation. The AI searches your actual documents and data before answering, so responses are accurate, sourced and grounded in your own business information instead of hallucinated. It's how we build AI that businesses can put in front of customers, staff and auditors without worrying about what it might say.