AI built around your business, your data and your hardware
From AI assistants and process automation to private LLMs trained on your own documents. We design, build and host AI that fits how your organisation actually works. Cloud, private cloud or on-premise hardware we supply and manage.
Start with the work that slows your organisation down
Every organisation has processes buried in documents, inboxes, spreadsheets and systems that don't talk to each other. We find that repetitive, knowledge-heavy work (reviewing contracts, answering internal questions, processing forms, pulling reports) and use large language models to take it off your team's plate.
What makes AI useful is its connection to your real data. We've spent over a decade building integrations across finance, CRM, practice management and custom platforms. We build AI on those same foundations: it can search millions of pages of your documents, take action in your systems and hand off to a person when it should.
When data can't leave the building, we bring the AI to it. We supply, install and manage GPU servers and run open-source LLMs privately on your premises or in a dedicated cloud, and we can fine-tune or train models on your own data so they speak your language.
End-to-end AI, from use case to hardware
We cover the whole stack: the problem, the data, the model, the software around it, and the infrastructure it runs on.
Internal process automation
Emails, forms, approvals and hand-offs read and actioned by AI, then routed into the right system with humans in the loop.
Document intelligence at scale
Search, summarise and extract data from thousands of contracts, reports, policies and PDFs, with answers that cite the source.
Enterprise knowledge assistants
A private assistant for staff that answers questions from your manuals, procedures and past work, securely and accurately.
Big data & analytics
Turn large, messy datasets into insight: classification, trend detection, forecasting and plain-English reporting on top of your data.
Custom & fine-tuned models
Models trained or fine-tuned on your own data for classification, extraction or domain-specific language your team uses every day.
Open-source AI
Llama, Mistral, Qwen, Gemma and other open models deployed, customised and supported. No per-seat licences, no vendor lock-in and full control over the model.
Private LLMs & AI hardware
GPU servers supplied, installed and managed, running open-source models on-premise or in a dedicated cloud, so your data never leaves your control.
AI agents & workflows
Multi-step agents that research, draft, check and file work across your tools, with approval steps where it matters.
AI inside your product
Add search, summarisation, drafting or recommendations to your SaaS platform, portal or app.
Customer-facing assistants
Website and support assistants trained on your content that answer accurately, capture leads and escalate to a person.
Your AI, wherever your data needs to live
Choose the model that fits your security, compliance and budget. We can start in the cloud and move on-premise later without rebuilding.
Cloud AI
Leading models from Anthropic, OpenAI, Azure or AWS through business-grade APIs, where your data isn't used for training.
Private cloud
Dedicated models in an isolated cloud environment, with Australian hosting available for sensitive data.
On-premise hardware
We size, supply, install and manage GPU servers running open-source LLMs like Llama, Mistral and Qwen inside your own network.
From idea to AI running in production
Discover
A workshop to map where your organisation loses time, what data is involved, and which tasks are good candidates for AI, and which aren't.
Assess data & infrastructure
We review data sources, volumes, privacy obligations and security, then recommend the right deployment: cloud API, private cloud or on-premise hardware.
Pilot
A working pilot on one use case, connected to real data, tested with your team and measured against a baseline.
Build, train & integrate
We productionise the pilot: model fine-tuning where it helps, guardrails, human-review steps, logging and full integration with your systems.
Deploy & train
Rollout on the chosen infrastructure (including hardware installation for on-premise) with staff training and clear rules for when AI acts.
Operate & improve
Ongoing monitoring of accuracy, usage, cost and hardware health, with regular retraining as your data and the models evolve.
Model-agnostic, integration-first
We choose the right model and infrastructure for each job and keep you portable, so you're never locked into one vendor as the technology moves.
The AI we build with
Commercial and open-source models, the clouds they run on and the tools that connect them to your business. We pick what fits each job.
ClaudeOpenAI
Google Gemini
Llama
Mistral AI
Qwen
DeepSeek
Hugging Face
Ollama
vLLM
Google Cloud
NVIDIA
PyTorch
Python
LangChain
Qdrant
Microsoft 365
Power BI
Zapier
Make
Product names and logos belong to their respective owners.
Is our data safe with AI?
Yes, when it's set up properly. We use business-grade APIs where data isn't used to train public models, restrict what the AI can access, and for sensitive data we deploy private models in a dedicated cloud or on your own hardware, so nothing leaves your control.
Can you run an LLM on our own servers?
Yes. We size, supply, install and manage GPU hardware and deploy open-source models such as Llama or Mistral on-premise or in a private cloud. You get the capability of a modern LLM without sending data to a third party.
Can you train a model on our own data?
Yes. Depending on the goal we fine-tune an existing model on your documents and examples, or train a smaller custom model for tasks like classification and extraction. We'll tell you honestly when retrieval over your documents is the better, cheaper option.
We have huge volumes of documents. Can AI handle that?
Yes. We build ingestion pipelines and search indexes that handle hundreds of thousands of documents, so the AI can find, summarise and cite the right source in seconds.
Will AI replace our staff?
That's not the goal. The best results come from removing repetitive work so people spend their time on judgement, clients and higher-value tasks. We design AI to assist and hand off, not act unchecked.
How much does an AI project cost?
Most clients start with a discovery workshop and a fixed-price pilot. Cloud usage costs are usually modest; on-premise hardware is quoted up front with a clear comparison against cloud running costs.
How long until we see results?
A pilot on a single use case typically takes a few weeks. On-premise deployments add hardware lead time, which we plan in from day one.
Why choose open-source AI over ChatGPT or Claude?
Open-source models like Llama, Mistral and Qwen can run entirely on your own infrastructure, be fine-tuned freely on your data, and carry no per-user licence fees. They're ideal when privacy, cost at scale or customisation matter most. Commercial models still lead on some complex tasks, so we often combine both.
Which AI model do you use?
Whichever fits the task: Claude, OpenAI, Azure, or open-source models like Llama and Mistral. We build so the model can be swapped later without rebuilding the solution.
Let's find where AI will make the biggest difference
Tell us about your processes, your data and any constraints on where it can live. We'll come back with practical options, costs and the savings they'd deliver.
