Services
What we build.
Every engagement starts with your problem, not a pre-packaged solution. Here's where we create the most value.
Computer Vision & Image Detection
See what humans miss
Aris Detect
Production SaaS · Drone-based inspection
A full property inspection platform processing drone imagery with ML-powered damage detection. Real inspectors depend on it daily across Australia.
We trained custom YOLO models on domain-specific datasets, deployed them on GPU infrastructure, and built a real-time multi-user platform around them. Not a demo -- a product people pay for and use every day.
Detection
Custom YOLO models
Inference
RunPod + AWS Lambda
Collaboration
Real-time multi-user
The stack
Aris Detect is how we cut our teeth, but the same approach applies anywhere visual inspection matters -- manufacturing quality control, agricultural monitoring, construction progress tracking, retail shelf analysis. If a human is looking at images to make a decision, we can usually automate the looking part.
AI Agents &
Autonomous Systems
Systems that work while you sleep
Not chatbots. These are software systems that monitor, decide, and act -- around the clock, without human supervision. They handle the operational burden that would otherwise require someone watching a screen.
We run agents in production ourselves. The ones listed here aren't hypothetical -- they're active right now, handling real work across our own systems.
- Polls user feedback every 30 minutes
- Creates Trello cards from bug reports
- Auto-fixes issues and submits pull requests
- Triages by severity -- critical bugs get immediate PRs
- Monitors Discord channels for research requests
- Maintains a living wiki of project knowledge
- Runs scheduled scouts for industry news
- Handles scheduling and coordination tasks
- Watches for merged PRs on protected branches
- Runs test suites across 10 environments
- Promotes builds through staging to production
These are systems we built for ourselves and run daily. When we build agents for clients, we apply the same architecture: event-driven triggers, structured decision-making, human escalation paths, and proper observability so you know what they're doing and why.
Talk to us about autonomous systemsMachine Learning Engineering
From experiment to production
You have a model. It works in a notebook. Here's the distance between that and a production system your business can rely on.
The notebook
Promising results on a test dataset. This is where most ML projects stall -- not because the model is wrong, but because nobody planned for what comes next.
Data pipeline
Production data is messy. We build ingestion pipelines that validate, transform, and survive first contact with reality.
Infrastructure
GPU provisioning on RunPod or AWS. Docker containers, model serving endpoints, cost optimisation -- the boring stuff that keeps inference alive at 3am.
Deployment
CI/CD that tests, packages, and deploys model updates without downtime. A/B testing and rollback for when retraining makes things worse.
Operations
Monitoring for model drift, data quality, and latency. Automated retraining when performance degrades. Alerting for things that need a human.
We handle the full path -- or jump in wherever you're stuck. If you have a model and need infrastructure, we start at stage 3. If you need everything, we start at the data.
Discuss your ML projectBusiness Process Automation
Stop paying humans to copy-paste
Before
Three people spent half their day pulling data from one system, reformatting it in a spreadsheet, and uploading it to another. Every day. For years.
After
A webhook triggers when new data arrives. A pipeline validates, transforms, and routes it automatically. The three people now work on things that actually need a human brain.
What else we build
Report generation. Automated property inspection reports from drone data, ML analysis, and inspector notes -- compiled and formatted without anyone touching a template.
Real-time collaboration. WebSocket systems that let multiple users work on the same data simultaneously, with changes propagating instantly.
Document processing. PDF extraction, classification, and routing. Take a pile of unstructured documents and turn them into structured data your systems can act on.
The common thread
Every automation project we take on follows the same test: is a human doing something a computer should be doing? If yes, we figure out the simplest reliable way to automate it. Sometimes that's AI. Sometimes it's just a well-designed webhook pipeline. We'll tell you which.
Not sure which service fits?
That's exactly what the conversation is for. Tell us what's eating your time and we'll figure out the right approach together.
Start a conversation