Most AI pilots work. Very few survive production.
The demo is rarely the hard part. The hard part is what follows: evaluating whether the system is actually right often enough, controlling what it costs per request at real volume, handling the cases it gets wrong, and being able to explain a decision to a regulator or a customer. We build AI systems with those questions answered up front, because retrofitting them is far more expensive than designing for them.
What we build
Four areas, chosen because they are where projects actually fail.
AI strategy and use-case selection
Which problems justify AI, which are better served by a query and a rule, and which are not ready because the underlying data is not. Sequenced by value and feasibility, with the cases we recommend against written down alongside the ones we recommend.
RAG and agent systems
Retrieval pipelines, chunking and embedding strategy, tool use and orchestration, and the human checkpoints that belong in any workflow with consequences. Built against your data, not a demo corpus.
Evaluation and observability
An evaluation set that reflects your real distribution, regression testing on every prompt and model change, and tracing so a bad answer can be traced to its cause. Without this you are not operating a system, you are hoping.
Deployment, scaling and unit cost
Model serving, routing between models by task difficulty, caching, and cost per request tracked from the first week. The same FinOps discipline we apply to cloud, applied to inference.
Governance
The question that stops deployments is rarely technical.
It is usually: where did the training data come from, who can see the inputs, what happens when the model is wrong, and can we show our reasoning if challenged. Australian organisations carry these obligations under the Privacy Act, and regulated industries carry more. We build the data lineage, access boundaries, human-in-the-loop checkpoints and audit trail into the system rather than documenting around it afterwards.
How an engagement runs
Prove it is worth building before building it.
The first phase frequently concludes that a use case should not proceed. That is a successful outcome, not a failed one.
1. Frame
Define the decision the system supports, what "right" means, and the threshold worth clearing. Output: success criteria agreed before any build.
2. Prove
Narrow prototype against real data, measured on the agreed criteria. Output: evidence, and a recommendation that may be to stop.
3. Harden
Evaluation harness, guardrails, failure handling, observability, cost per request. Output: a system that can be operated by people who did not build it.
4. Run
Deploy, monitor quality drift as much as uptime, and review unit economics. Output: an operating rhythm, not a launch event.
Bring us the use case you are unsure about.
Those conversations are more useful than the ones about use cases already decided. If it should not be built, we will tell you in the first session.
Book a scoping call