Engineering
When AI is the product, it has to outlast the model it was built on. We build AI products for Australian businesses and product teams, and we build our own. Architecture, evaluation and model portability come first, because that is the engineering that turns a demo into something you can release every Tuesday.
Artrilogic AI
Answers from our published service information
Artrilogic AI
Answers from our published service information
01What you are trying to do
A well-built AI feature can set the product apart and keep customers coming back, as long as the team can tell whether each change makes it better.
02What gets in the way
It works in the demo and mostly works in production. Customer success hears one bad story, sales hears a good one, and the team patches around both until the product becomes a list of overrides.
03How we help
Data flows, an evaluation harness and observability come before the first prompt is written. With those in place, you can adopt a better or cheaper model when one arrives without rebuilding the product.
How we approach AI products
Most AI products that fail in their second year fail because the architecture was the prompt. We design the data flows, evaluation harness and observability before any user-facing copy.
If you cannot tell whether the next model release made your product better or worse, what you have is a chat interface rather than a product. An evaluation harness gives the team that answer on every release.
The strongest model for your task today will probably not be the strongest in six months. We build the abstraction layer so your product can move to the better option without rebuilding the product around it, which keeps cost and quality in your hands.
Some product features genuinely benefit from AI. Others are better as deterministic logic with a UX that admits it, and are cheaper to run and easier to test. We will say which is which on your feature list.
Evidence
Not every product we build uses AI. This one shows the engineering underneath: a consumer health app designed from day one for a high-volume user base.
Delivered client work
A consumer-facing health app on Google Cloud infrastructure, designed from day one for a high-volume user base.
Read the case studyOur own products
Building our own products keeps our engineering advice grounded. We describe them here as practice, not as results.
CV intelligence for engineering hiring, in active build on the same Next.js, Docker and Caddy stack we recommend to customers.
Visit CalibratedCV.comA digital front desk for service businesses, where the first interaction decides whether a customer proceeds or moves on.
Read about DogmaEngageA two-hour architecture session with a senior engineer. Bring the spec and we will bring the questions that decide whether it holds up in production.
