AI & Machine Learning
Model systems built for production reliability, measurable quality, and controlled deployment velocity.
- Model training pipelines
- MLOps and deployment automation
- Drift detection and retraining
AI · Machine Learning · IoT Engineering
AI, machine learning, and IoT development for Australian businesses.
We build AI, machine learning, and IoT systems for Australian businesses. From model training to live deployment — using Python, TensorFlow, and cloud-native tools. Data that drives real results.
Machine LearningComputer VisionNLP & LLMsIoT & EdgeMLOpsIoT Cloud
200+
Models in production
6wk
Avg. time to production
500K+
IoT devices managed
2TB+
Data processed daily
Live Intelligence Layer
Streaming telemetry · model inference · alerts
Accuracy
97.4%
Latency
14ms
Throughput
2.4K/s
Connected devices
Edge Node 01Sensor ArrayGateway Delivery Streams
Two integrated practices. One unified delivery model.
Our AI and IoT teams work together. One delivery model. Data, edge, cloud, and MLOps — all covered.
Unified Delivery Model
DataEdgeCloudMLOps
IoT & Connected Systems
From embedded firmware to cloud-scale telemetry and fleet observability for connected operations.
- Device firmware and sensors
- MQTT telemetry ingestion
- Fleet management and observability
Unified Delivery ModelDataEdgeCloudMLOps
Unified Output
Production-ready systems
Measurable outcomes
Continuous improvement
Operational reliability
Our Services
AI and IoT development capabilities for production systems.
Everything you need to go from concept to live system. Built for Australian businesses. Delivered on time.
Machine Learning Engineering
Model training, MLOps, and production deployment using Python, TensorFlow, and PyTorch — built for Melbourne and Australian enterprise systems.
Data Pipeline & Engineering
Ingestion, transformation, and real-time streams designed for reliability at scale.
Computer Vision
Image recognition, object detection, and edge inference for operational workflows.
Natural Language Processing
LLMs, RAG pipelines, embeddings, and fine-tuning for domain-specific intelligence.
IoT Device & Firmware
Embedded systems, RTOS, and sensor integration across constrained hardware environments.
IoT Platform & Cloud
Telemetry, MQTT, device management, and AWS IoT architecture for secure operations.
Use Case Showcase
Real results from AI and IoT systems we've built.
Real-world examples. AI and IoT systems running in production today for Australian and global businesses.
Industrial Operations
Predictive Maintenance
IoT sensors and anomaly detection models identify failure signatures before breakdown events occur.
IoT SensorsTime-Series MLAlerting
Outcome
73%
Less unplanned downtime
Manufacturing Quality
Computer Vision QA
Camera-based defect detection replaces manual inspection and maintains quality at line speed.
Edge InferenceObject DetectionQA Pipeline
Outcome
99.2%
Detection accuracy
Retail & Supply Chain
Demand Forecasting
Forecasting models reduce inventory waste while improving replenishment timing across channels.
Forecast ModelsData PipelineDemand Signals
Outcome
31%
Less inventory waste
Production Metrics
AI/ML and IoT delivery at operating scale.
Execution measured in deployed systems, data throughput, and sustained operational reliability.
0+
Models in Production
0 weeks
Model-to-Prod Time
0K+
IoT Devices Managed
0TB+
Data Processed Daily
AI/ML + IoT delivery model comparison.
Clear differences in speed, specialist access, and operating reliability.
| Criteria | Build In-house | Our Model | Generic Consulting |
|---|---|---|---|
| Time to first model in production | 4-9 months | 6 weeks avg | 3-6 months |
| Access to specialist ML talent | Limited | Immediate | Variable |
| Ongoing model monitoring | Often ad-hoc | Built-in | Optional add-on |
| IoT hardware expertise | Rare | Deep bench | Inconsistent |
| Integration with existing stack | High | High | Medium |
| Cost transparency | Low visibility | Clear sprint metrics | Mixed pricing |
Time to first model in production
In-house
4-9 months
Our Model
6 weeks avg
Consulting
3-6 months
Access to specialist ML talent
In-house
Limited
Our Model
Immediate
Consulting
Variable
Ongoing model monitoring
In-house
Often ad-hoc
Our Model
Built-in
Consulting
Optional add-on
IoT hardware expertise
In-house
Rare
Our Model
Deep bench
Consulting
Inconsistent
Integration with existing stack
In-house
High
Our Model
High
Consulting
Medium
Cost transparency
In-house
Low visibility
Our Model
Clear sprint metrics
Consulting
Mixed pricing
Our process.
Technical,
measurable,
production-ready.
STEP 1
Discovery & Data Audit
We assess your data maturity, infrastructure, and ML readiness before defining scope.
STEP 2
Solution Architecture
We design the model pipeline, IoT topology, and reliability controls for your stack.
STEP 3
Build & Validate
Engineers build, train, and validate in your environment with measurable acceptance criteria.
STEP 4
Deploy & Monitor
Production release with monitoring, alerting, and operational runbooks from day one.
Proof from teams shipping AI/ML and IoT systems with operational reliability.
Here are just a handful of places where they work.
Models Running In Production At
Frequently asked questions for AI/ML + IoT delivery
Ready to start?
Talk to an AI/IoT engineer today.
Tell us what you are building and we will connect you with the right capability in under 4 business hours.
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