Australian founders and operators come to us with one of three problems: a new mobile app that has to feel native from day one, a rebuild of an existing app that can't keep up with growth, or an AI feature — a support agent, a document pipeline, a recommendation engine — added to a product that already has real users. All three start the same way: a discovery call where we map your existing stack, your data sources, and the constraint that actually matters, whether that's a fundraising deadline, a compliance review, or a competitor launch.
From there we scope in writing. You get a fixed price and a realistic timeline before any code is written, not a range. For a first version of a mobile app, that's typically 4 to 8 weeks depending on how much is genuinely new versus adapted from one of our existing builds. For an AI feature added to a live product, it's usually 3 to 6 weeks, most of which goes into wiring the AI to your actual data, your CRM, your support history, your product catalogue, rather than the model itself. The model is rarely the hard part.
Integration is where most AI projects actually fail, so it's where we spend the most engineering time up front: mapping your existing APIs, agreeing on auth and rate limits with whatever system you're connecting to, and building a fallback path for when the AI genuinely doesn't know the answer instead of letting it guess. We test against your real data before launch, not a demo dataset. Every engagement ends with a full handover, source code, repo access, and documentation, transferred to you, so nothing you paid for stays locked to us.
If you're weighing whether to build in-house, hire a local agency, or bring in a senior-led remote team, book a discovery call and we'll give you an honest read on what the project actually needs, even if that means telling you it's smaller than you think.
Case study
Real client region: UK
A commercial fleet operator, 24,000 vehicles, legacy telemetry that couldn't keep up
Problem: 30-second GPS polling into a single MySQL instance meant dashboard queries took 12–40 seconds and dispatchers had zero visibility into driver behaviour, harsh braking, idling, speeding, that was costing the business fuel spend at scale.
What we built: An event-driven telemetry pipeline (Kafka, Flink, TimescaleDB, Redis) processing 6 million events a day, plus a predictive routing engine that re-optimises routes every 15 minutes.
Result: An 18% cut in fuel spend (£3.8M a year), sub-800ms end-to-end event latency, and dashboard queries down from 12–40 seconds to 280ms, with 99.95% platform uptime in the 11 months since launch.