SaaS and technology teams move fast — but production AI, multi-tenant isolation, and platform reliability cannot be afterthoughts. We build the infrastructure layer that lets you ship agents, automation, and ML features on schedules your product team can actually hit, without re-architecting every quarter.
What teams face
AI features that break at scale
Demo-grade integrations work in staging. Production needs tenant isolation, rate limiting, observability, and cost controls that survive your first enterprise customer or traffic spike.
MLOps debt before product-market fit
Teams often ship models manually — notebooks, ad-hoc deployments, no versioning. As usage grows, retraining, monitoring, and rollback become blockers instead of competitive advantages.
Platform teams stretched across too many surfaces
Kubernetes, CI/CD, security patches, and on-call rotation compete with feature work. Without dedicated platform engineering, reliability and velocity trade off against each other constantly.
What we build
Multi-tenant platform architecture
Isolation, auth, and resource boundaries designed for SaaS from the start — so new tenants and AI workloads do not compromise neighbours or blow up your unit economics.
Production MLOps pipelines
Automated labelling, training, deployment, and monitoring on AWS — SageMaker, Step Functions, and EKS patterns that reduce manual ops as model count grows.
Applied AI and agent engineering
Customer-facing agents and internal automation wired into your product stack with proper APIs, logging, and rollback — not a chatbot iframe pasted onto the dashboard.
Related work
Sports membership platform as embedded engineering team
Eight months embedded with an Australian sports membership app: secure member accounts, scalable video delivery, fixes after independent security testing, and monitoring the internal team can run day to day.
View case studiesAI ad-video production pipeline
A repeatable video production workflow for an Australian advertising agency. Add a presenter, script, and voice description; get social-ready videos back. No studio, actors, or usage fees.
View case studiesFurther reading
Tech Stack for Scaling AI-Powered Businesses
Infrastructure choices that keep pace as AI features become core to your product.
MLOps Teams in Australia — Complete Guide
How to structure MLOps for production ML across mining, finance, retail, and tech.
Enterprise AI Implementation Guide
Phased approach to shipping AI in production without stalling product delivery.