Services · AI systems
Scalar Labs builds AI systems and agents for Australian companies: agent pipelines, LLM features and automation that hold up in production. We run AI agents through our own operations every day, so we know the difference between a demo and a system.
Agent pipelines, where models do multi-step work with tools, supervised and checked. LLM features inside an existing product: drafting, extraction, search and classification. And automation of internal operations, which is where the return usually arrives fastest, and the kind we run on ourselves.
Around each of those sits evaluation, monitoring, fallbacks and data handling. Most of the effort goes there, and it is the layer that decides whether the feature holds up in production.
For: companies putting AI into production systems.
Every AI engagement starts with a scoping pass on whether the thing should be built at all: what the automation is worth, and what a wrong answer costs when the model produces one. A scoping pass can end with a recommendation not to build, in writing. We would rather say that at the start than after the budget is spent. When the case holds, the build runs like the rest of our product work: agreed scope, evaluation before rollout, and a handover your team can run.
Whichever fits the job, and the answer changes as the market does. The system is designed so the model is a replaceable part: the pipeline, the evaluation and the data handling are yours, the model behind them is a choice you can revisit.
It depends on which stage you buy. A scoping pass is fixed at A$5,000 to A$10,000 by the size of the problem, and it prices the rest: it tells you what the automation is worth before you commit to building it. A proof of concept runs from A$25,000; a production build is scoped separately. Every number here moves with scope, and the figure is agreed before work starts.
Done usefully, it is an engineering exercise rather than a quiz: the data you hold, where it lives, what your systems can expose to a model, and which processes are worth automating first. Ours is the scoping pass described above.
When the task needs an answer that is right every time, not usually. When a database query already does the job. When the volume never repays the build.
That is a design decision made up front, and it is written into the architecture: which data reaches a model, under what agreement, and what never leaves your infrastructure. Enterprise model agreements with no-training terms exist, and so do self-hosted options where the data stays in Australia, or does not travel at all.
Every day, for research, code and the running of the company.
Client work is confidential as a rule, so you'll find sectors here rather than names. We can go into specifics on a call, and put you in touch with references.
Technical lead for an Australian legal tech platform: architecture, an AI pipeline, and delivery from first commit to a production MVP.
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