AI that works on your real data, not just in a demo
Plenty of AI projects look good in a presentation and stall before launch. We build the part that gets them into production: connected to your data, tightly scoped and tested for accuracy.
What we build
Four types of AI work, each designed around a real business process and a measurable result.
Knowledge assistants
Search and answers across the documents your team already uses.
Private document search
Ask questions across policies, manuals and contracts, with the same access permissions your staff already have.
Answers with sources
Every answer links back to the page it came from, so people can check it in seconds.
Mixed formats
Reads PDFs, spreadsheets, scanned forms and images, not only clean text.
AI agents
Agents with one clear job, the right tools and a measurable result.
Customer support agents
Resolve common enquiries on your website, email or WhatsApp and pass the rest to your team with full context.
Sales and lead qualification
Respond to new leads instantly, ask the right questions and book qualified meetings into your calendar.
Human approval built in
The agent does the groundwork and a person makes the final call on anything that matters.
Workflow automation
Replace manual, repetitive steps with systems that adapt when something unexpected happens.
Document and invoice processing
Extract data from invoices, forms and emails, match it against records and flag exceptions.
Connected to your tools
Integrations with CRMs, Microsoft 365, Google Workspace, Xero and MYOB, with a log of every action taken.
Foundations
The groundwork that makes AI safe to put in front of customers.
Model selection and fine-tuning
Choosing, and where it helps, adapting the right model for your cost, speed and accuracy needs.
Evaluation and guardrails
Test sets that measure accuracy, catch made-up answers and block unsafe output before launch.
Privacy and data residency
Options to keep data in Australian cloud regions and out of model training.
Models and tools we work with
Claude, GPT, Gemini, Llama and Mistral models. LangChain, LangGraph and CrewAI for agents. PostgreSQL with pgvector, Qdrant and Pinecone for retrieval. n8n and Make for workflows. We choose per project and keep each piece swappable.
Where AI helps most
Practical examples of the work AI can take off your team's plate.
- Mining & resources
- A safety assistant that answers questions from procedures, permits and site manuals, on a phone in the field.
- Real estate
- Instant replies to property enquiries, lead qualification and inspection bookings straight into the agent's calendar.
- Health & allied health
- Automated intake forms, appointment reminders and summaries of patient messages for staff to review.
- Education & training
- A student support assistant for enrolment, course and policy questions, available around the clock.
- Finance & professional services
- Data extraction from statements, invoices and forms, with exceptions flagged for a person to check.
- Retail & e-commerce
- Product recommendations, order tracking and returns handled in chat.
How an AI project runs
We start small, prove value on your data, then scale what works.
Find the right process
We look at where the hours go and which decisions repeat, then choose a process where AI can make a measurable difference.
Prove it on your data
A small prototype using your real documents and edge cases, scored against an agreed definition of a good answer.
Launch with guardrails
Accuracy tests, permission controls, fallbacks and human hand-off built in before anyone relies on it.
Measure and improve
We monitor cost, speed and answer quality in production, tune it, and document it for your team.
AI questions, answered
The things business owners most often ask before starting an AI project.
Will our data be used to train AI models?
We set up AI services on business plans and settings that keep your data out of model training, and can host in Australian cloud regions where the provider supports it.
How accurate will it be?
We agree what a correct answer looks like at the start, build a test set from your real questions, and measure accuracy before launch.
What does it cost to run?
Running costs depend on usage. We estimate them during the prototype so you know the monthly cost before you commit.
What if AI isn't the right fix?
We'll tell you. Sometimes a simple automation or a better form solves the problem for less.
Have a process worth automating?
Bring the workflow and the data. We'll tell you honestly whether AI is the right tool, and what it would take.