About our AI software practice

Who we are, why we started, and how we think about building technology that earns its keep.

How we got here

Uplift AI Systems grew out of a frustration that two of our founders shared while working inside large consulting firms. They kept watching AI projects stall because the scope was too grand, the data was never clean enough, and nobody on the delivery team had actually run a business process end to end. Projects would ship a model, declare victory, then quietly fall apart when the client tried to use it on a Tuesday morning with real data and real deadlines.

So in 2022 they left and started something smaller. The idea was simple: pick one well-defined problem, prove the model works on real records within three weeks, and only then talk about scaling. That discipline has stayed with us as the team has grown.

We are based in East Kiarafield, Victoria. Our clients range from mid-size logistics operators to healthcare providers and agricultural cooperatives across Australia. Most engagements last between two and five months, and we measure success by whether the system is still running six months after handover, not by how impressive the demo looked on day one.

Our team collaborating around a whiteboard

What guides our work

We do not have a manifesto pinned to the wall. We do have a few habits that shape every project.

Prove it early

Every engagement begins with a proof of concept built on your actual data. If the model cannot beat your current spreadsheet or manual process within three weeks, we say so and stop billing. About one in five discovery calls ends with us recommending a simpler, non-AI solution. That is fine by us; it means the other four projects genuinely benefit from machine learning.

Own the outcome

We do not hand over a Jupyter notebook and wish you luck. Our deliverables are production systems: containerised, monitored, documented. The person who built the model also writes the deployment scripts and sits in on the first week of live operation. When something breaks at 7 a.m., we want to know before you do.

Keep it legible

If a stakeholder cannot understand why the model made a particular prediction, the model is not ready to ship. We favour interpretable approaches where possible and add explanation layers when deep learning is the only viable option. Regulatory compliance in healthcare and finance demands this, and we think every industry deserves the same standard.

Respect the data

Client data never leaves the agreed infrastructure. We sign data processing agreements before the first file transfer. Access controls are role-based, audit-logged and reviewed quarterly. When an engagement ends, we delete all client data from our systems within 30 days unless a retention agreement says otherwise.

The people behind the code

A small team that covers machine learning, data engineering and product design.

Portrait of Priya Nair

Priya Nair

Co-founder and ML lead

Priya spent six years building recommendation engines at a Melbourne e-commerce company before co-founding Uplift AI Systems. She leads model architecture decisions and personally reviews every production deployment.

Portrait of Daniel Marsh

Daniel Marsh

Co-founder and data engineer

Daniel designed ETL pipelines for a national logistics firm before joining Priya to start this company. He handles infrastructure, cloud architecture and the less glamorous but essential work of making sure data arrives clean and on time.

Portrait of Sasha Wellings

Sasha Wellings

Product designer

Sasha translates model outputs into dashboards and interfaces that non-technical users can actually work with. Before joining us, she designed clinical decision-support tools for a health-tech startup in Sydney.

By the numbers

A snapshot of what we have delivered since launching in mid-2022.

34

Projects completed

12

Ongoing retainer clients

96 %

Models still in production after 6 months

4.2 wk

Average proof-of-concept delivery time

The 96 % figure matters most to us. It means the systems we ship keep running after the initial excitement fades, which is the only honest measure of whether an AI project worked. Two of the 34 projects were deliberately sunset by the client after a business pivot, and one was replaced by an in-house team we helped train. We count all three as successful outcomes because the client got what they needed at the time.

We track these numbers quarterly and share them with prospective clients during the discovery call. If you want references from a specific industry, ask and we will connect you directly with a past client who has agreed to take those calls.

Ready to talk about your project?

Drop us a line and we will set up a 30-minute discovery call. No slide decks, no pressure.

Get in touch