AI & Digital Engineering Consultancy
Modernise. Build. Ship.
Melbourne-headquartered, working remote-first with teams Australia-wide. We take businesses from legacy systems to production AI — modernisation, data infrastructure, intelligent software, and the data science to prove it works.
AI that delivers outcomes, not demos
Production AI capabilities with real metrics from real deployments.
AI Agents & Orchestration
Multi-agent systems that plan, execute, and adapt complex workflows autonomously.
Computer Vision & Document AI
Image recognition, document extraction, quality inspection, and OCR pipelines.
LLM Integration & RAG
Retrieval-augmented generation over your data. Sourced answers, not hallucinations.
Predictive Analytics
Forecast demand, detect anomalies, predict churn, and optimise operations with ML.
Conversational AI
Voice agents, chatbots, and copilots that handle real conversations with real users.
MLOps & Responsible AI
Monitoring, drift detection, guardrails, and governance for production AI systems.
What we build
AI-native product capabilities from strategy through to production. One team, end to end.
Measured in outcomes, not effort
Real numbers from production AI systems we’ve built and deployed.
50K documents/month
8 hours → 20 minutes daily processing
AI pipeline that extracts structured data from contracts, invoices, and compliance documents with 99%+ accuracy.
84% less downtime
72-hour failure prediction window
Sensor-data ML model predicting equipment failures before they happen. Deployed across 3 manufacturing facilities.
2,000 invoices/month
97% accuracy, fully autonomous
Multi-agent system that processes invoices, matches to POs, flags discrepancies, and drafts responses without human intervention.
We build with AI ourselves.
Horizon Labs is also a venture studio. We build and run our own products alongside client work — AI-powered platforms, data infrastructure, content systems, and analytics tools. That hands-on experience with both AI and digital engineering shapes how we approach every client engagement.
AI in Our Own Stack
We run production AI systems internally — so we know what works, what breaks, and what matters when you’re the one on call at 3am.
Concept to Revenue
We take AI product ideas from validation through to revenue-generating products with real users and real data.
Practitioner Mindset
Every client engagement benefits from the operational experience we get building and running our own AI products.
Engineers who ship
Horizon Labs is an AI and digital engineering consultancy. We started building custom software in 2018, went deep on AI as it moved into production, and now cover the full journey — modernisation, data infrastructure, AI, and measurement.
Our team works across application modernisation, cloud infrastructure, data pipelines, LLM-powered platforms, and intelligent automation. We bring the delivery experience of a mature consultancy with genuine, hands-on AI engineering depth.
Why teams choose us
- End-to-end: modernisation, data infrastructure, AI, and measurement under one roof
- AI engineering depth: custom models, fine-tuning, RAG, agents, and MLOps
- Legacy modernisation: monolith to microservices, cloud-native, API-first
- Data infrastructure: pipelines, warehousing, governance — the foundation AI needs
- Your IP, your code — no platform lock-in, no vendor dependency
- Melbourne-headquartered, remote-first across Australia, with a bias toward shipping
Teams we’ve worked with
From enterprises to startups, we partner with teams building digital products and AI-powered solutions.
How we work
From discovery to scale. Working AI products every two weeks.
Discover
We map your workflows, data assets, and business goals to find where AI creates real value. Feasibility testing on your actual data — not theoretical potential.
Design
AI-native UX, model architecture, and evaluation criteria — designed together. We prototype with real AI outputs so you see exactly what users will experience.
Build
Two-week sprints. Working AI features every fortnight. Models with evaluation suites, monitoring, and guardrails — production-grade from the first deployment.
Operate
AI doesn’t end at launch. We monitor accuracy, detect drift, retrain models, and optimise costs. Your AI gets better over time, not worse.
Our AI stack
Model-agnostic. Framework-flexible. Production-grade.
What we’re thinking about
Practitioner perspectives on production AI, modernisation, and the operational discipline that separates demos from products.
Context Engineering for LLM Apps: Beyond Prompt Templates
Prompt templates are where LLM applications start. Context engineering is what makes them work reliably in production. This article covers the four core levers — retrieval, compression, memory, and ordering — and how to build a context pipeline that produces consistent, cost-efficient model behaviour at scale.
Data Contracts: Stopping Pipeline Breakage Before It Starts
Silent schema drift is one of the most common and costly causes of broken data pipelines and degraded AI models — and it rarely announces itself. Data contracts are the structural mechanism that catches upstream changes before they reach production, enforcing schema, quality, and freshness expectations at the producer level. This post explains what data contracts are, how to implement them with modern tooling, and why they are foundational to reliable AI and analytics infrastructure.
AI Incident Response: What to Do When Your Model Fails in Production
When an AI model fails in production, the failure is often silent — no error code, just degrading outputs. This guide is a practical incident response playbook for ML and LLM systems: detection, severity classification, rollback, stakeholder communication, and post-incident review, built for technical leaders who need to extend their existing incident processes to cover AI-specific failure modes.