For organisations with existing infrastructure. We embed AI into what's already there - legacy systems, compliance-heavy workflows, enterprise tools - without asking you to start over.
Engineering the Future for High-Growth Startups and Enterprises
Overview
Enterprise AI rarely fails on the model. It fails on the legacy system it has to live inside, the compliance regime it has to satisfy, and the data that was never ready for it. We embed AI into what you already run - ERP, CRM, core platforms, and regulated workflows - without asking you to rip anything out.
We've done the genuinely hard version: re-architecting the ledger stack of an ASX-listed payments company processing over $80 billion a year, under APRA and PCI DSS, with every decision documented to audit standard. Model-agnostic, security-first, and built for environments where downtime isn't an option.
case studies
Explore our case studies showcasing scalable architectures, AI-driven solutions, and modern engineering practices.
Australian financial services and credit provider
A multi-agent pipeline that interprets inconsistent banking data, structures it for credit assessment and hands it to an underwriting agent, with confidence checks, full traceability and human authority over every consequential decision.
Listed payments company
Embedding at C-suite level to lead the re-architecture of a listed payments business: a single canonical ledger service, a unified API contract layer, and a move from hybrid to fully native mobile.
Cloud-native core banking platform
Providing in-market technical leadership for a global core banking platform entering Australia - owning client implementations end to end, from ledger architecture and card program setup to the regulatory evidence local institutions require.
API layers and middleware that let AI work alongside what you already run, with no rip-and-replace.
Data residency, audit trails, and access controls built in, not bolted on.
Prompt injection, data leakage, and model access, threat-modelled before launch.
The work that makes AI possible on enterprise data that wasn't built for it.
AI inside the ERP, CRM, and internal tools your teams already use.
Migration planning that doesn't require betting the business.
Chosen for what it has to survive - throughput, audit, and the team maintaining it after we leave.
Model-agnostic by default. We choose per problem - on latency, cost, and accuracy - and keep the cost of switching low.
Interchangeable by design
Cloud-neutral. We work in whichever one you already run, and won't move you off it without a reason you'd accept.
FAQs
Yes - this is our specialty. We embed AI into existing enterprise infrastructure without rip-and-replace. We build API layers, middleware, and integration points that let AI work alongside your current ERP, CRM, data warehouse, and internal tools.
Every engagement starts with a compliance and security review. We design for data residency requirements, implement audit trails, enforce access controls, and ensure AI systems meet your regulatory obligations - whether that's SOC 2, ISO 27001, or industry-specific standards.
We've embedded AI into ERP systems, CRM platforms, data warehouses, compliance workflows, document management systems, and custom internal tools. The common thread is complex, existing environments where constraints are real and downtime is not an option.
No. We can work with on-premise, hybrid, or cloud infrastructure. We assess your current environment and design an integration path that adds AI capability incrementally - without requiring a full migration first.
We conduct threat modelling specific to AI - prompt injection prevention, data leakage controls, model access management, and output validation. Security review is part of every enterprise engagement, not an afterthought.
We start with a discovery phase - auditing your systems, data readiness, and compliance requirements. Then we design the integration architecture, build in phases with production guardrails, and hand over with full documentation and team training. We agree the scope and timeline for each phase before it starts.