Accorda Solutions
AI That Ships. Systems That Scale.
End-to-end machine learning engineering from architecture design to production deployment built by practitioners who have shipped ML at enterprise scale.
What we do
Most machine learning projects fail not because the models are wrong, but because they were never designed to run in production. They break under real data, fail to integrate with existing systems, and are impossible for teams to maintain. We build differently. Every engagement starts with the end state in mind: a system that works reliably, scales with your data, and your team can own.
We have shipped ML across some of Australia's most data-intensive environments REA Group, SEEK, and oOh!media solving problems in search ranking, recommendation, classification, and predictive modelling. We know what it takes to move from a notebook prototype to a deployed service that handles real traffic, real data drift, and real operational pressure.
Whether you are exploring ML for the first time or trying to rescue a stalled project, we bring the architecture thinking, engineering discipline, and delivery rigour to make it work. We do not hand you a model and walk away. We make sure it runs.
What you get
Production-ready ML systems
Models packaged, deployed, and monitored as real services not notebooks. We build pipelines that handle retraining, versioning, and rollback.
Faster time to value
Structured delivery that cuts the distance between idea and working system. We prioritise the use cases most likely to generate measurable business impact first.
Reduced technical debt
Systems designed to evolve: modular, documented, and built on patterns your team can extend without needing to start from scratch.
Operational confidence
Monitoring, alerting, and observability built in from day one so you know when your model is performing and when it is not.
How we work
A structured approach that moves fast without skipping the steps that matter.
- 01
Discovery and scoping
We map your data landscape, identify the highest-value ML use cases, and define what success looks like in production before writing a single line of model code.
- 02
Data assessment
We audit your data for quality, volume, and suitability. We surface gaps early and help you understand what is buildable now versus what requires more investment.
- 03
Architecture and model design
We design the end-to-end system: feature engineering, model selection, serving architecture, and the MLOps infrastructure that will keep it running reliably.
- 04
Build and iterate
We build in tight loops prototype, evaluate, refine using offline metrics tied directly to the business outcome you are optimising for.
- 05
Deploy, monitor, and hand off
We deploy to your cloud environment (AWS or GCP), instrument monitoring and alerting, and make sure your team can operate, retrain, and iterate without us.
Common questions
Ready to get started?
Tell us about your situation. We will respond within one business day.
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