AI
Custom AI Development and LLM Applications
We build custom AI features that make it to production and stay reliable there. From LLM applications and RAG to model integration, evaluation and MLOps.
The hard part of AI is rarely the model; it is everything around it. Getting a feature from a promising demo to something dependable means grounding it in your data, evaluating it honestly, controlling cost and latency, and operating it once it ships. We build custom AI systems with those realities in mind, so what you launch is measurable and maintainable rather than a prototype that quietly degrades.
Grounded answers
AI responses traceable to your own data and sources
Measured quality
features shipped with evaluation, not vibes
Controlled cost
token spend and latency managed to work at production scale
LLM applications grounded in your data
Most useful enterprise AI needs your own knowledge, not just a general model. We build retrieval-augmented generation pipelines that ground responses in your documents and data, with the chunking, embedding and retrieval quality that actually determines whether answers are trustworthy. We are deliberate about when retrieval, fine-tuning or plain prompting is the right tool.
- RAG pipelines with attention to retrieval quality, not just vector storage
- Grounding and citations so answers are traceable to sources
- Structured extraction, classification and summarisation
- A clear choice between prompting, retrieval and fine-tuning
Evaluation, safety and cost control
AI features fail quietly, so we treat evaluation as a first-class part of the build with test sets and metrics that reflect your actual task. We add guardrails against prompt injection and unsafe output, and we manage token cost and latency so the economics work at scale. Without this, an AI feature that demos well can become expensive and unreliable in production.
- Task-specific evaluation sets and regression testing
- Guardrails against prompt injection and unsafe responses
- Token cost, caching and latency optimisation
- Human-in-the-loop review where accuracy is critical
Model integration and MLOps
We integrate commercial and open models behind a clean abstraction so you are not locked to one provider, and we host open-weight models where data residency or cost demands it. Around that we build the MLOps to deploy, version, monitor and roll back models like any other production system. That keeps your AI observable and under control.
- Provider-agnostic integration to avoid lock-in
- Self-hosted open models where residency or cost requires it
- Deployment, versioning and rollback for models and prompts
- Monitoring of quality, drift, cost and latency in production
From proof of concept to production
We often start with a focused proof of concept to test feasibility against your real data before committing to a full build. If it holds up, we harden it into a production system with the evaluation, security and operations that a demo skips. If it does not, you have found that out cheaply.
Frequently asked questions
- Should we use a commercial API or a self-hosted open model?
- Commercial APIs usually give the best quality with the least operational burden and are the right starting point for most projects. Self-hosting an open-weight model makes sense when data residency, privacy or high-volume cost tips the balance. We build behind a provider-agnostic abstraction so this can change without rewriting your application.
- How do you stop an AI feature from producing wrong or unsafe answers?
- There is no single switch; it is a combination. We ground responses in your data with retrieval and citations, build task-specific evaluation sets to measure accuracy, add guardrails against prompt injection and unsafe output, and keep a human in the loop where the stakes justify it. We also monitor quality in production so drift is caught early.
- Do we need to fine-tune a model?
- Usually not first. Retrieval-augmented generation and good prompting solve most enterprise problems more cheaply and flexibly than fine-tuning. Fine-tuning earns its place for narrow, high-volume tasks with a specific style or format requirement. We test the simpler approaches before recommending it.
Related services
- AI AgentsWe build AI agents that actually do things: call your tools, work across systems, and complete multi-step tasks, with the guardrails and oversight to run them safely.
- Enterprise AIWe help large organisations turn AI from scattered experiments into governed, secure capability that delivers measurable value. Strategy, platform, governance and adoption.
- Backend DevelopmentBackend systems designed for correctness, scale and the day-to-day reality of operating them in production.
Industries we serve
- HealthcareHealthcare software where privacy, clinical safety and interoperability are designed in from the first sprint. We build patient-facing and clinical systems for Australian providers who cannot afford to get compliance wrong.
- GovernmentSecure, accessible digital services that meet the standards Australian government actually holds you to. We build for IRAP assessment, the Essential Eight and data sovereignty from day one.
- StartupsShip an MVP fast without building a mess you have to unpick at Series A. We help Australian startups and scale-ups get to market and then scale the architecture as traction demands.
From the blog
- Event-Driven Backend Patterns for Retail InventoryReal-time inventory is the hardest consistency problem in retail. Here is how to build an event-driven backend that survives duplicate deliveries, schema drift and channel drift.
- Designing Reliable AI Agents for Enterprise WorkMost agent projects die in the gap between a slick demo and a system an enterprise can actually depend on. Here is how we close it.
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