Systems that made it past the demo.
Selected work across AI agents, analytics, autonomous engineering, national research infrastructure, climate platforms, model-training loops, and audit-grade data workflows.
The common thread is not the domain. It is the level of responsibility: systems that need to produce useful outputs repeatedly, under constraints, with enough structure to be trusted by real users.
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ANALYTICS AGENT · APP
screenshot · 240×180 A production Shopify analytics product that turns GA4, Search Console, and store data into daily merchant decisions. Six core analysis agents are coordinated by a router to detect anomalies, surface opportunities, audit GA4 configuration, and produce a plain-English Mission Brief.
The product demonstrates Invenire's core operating pattern: bounded agents, structured outputs, recurring evaluation, operational telemetry, and commercial UX wrapped around the model layer.
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PLANT · MULTI-PROCESS RUN
terminal/htop view · 240×180 An autonomous engineering platform exploring how one senior operator can coordinate AI agents, externalised state, objective verification, and multi-service software delivery. Seven services across five languages, designed around agent-first workflows and completion checks rather than ad hoc prompting.
Plant is the R&D layer behind Invenire's AI-native engineering practice: how to use agents aggressively without losing architecture, reviewability, or control.
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ECOCOMMONS · PLATFORM
ARDC screenshot · 240×180 National-scale ecological and environmental modelling infrastructure. As Technical Lead and Solutions Architect at Griffith University, Arve helped deliver a reproducible modelling platform across nine partner institutions and a multi-year funding environment.
This work matters because production AI has the same hard requirements as serious research infrastructure: provenance, reproducibility, permissions, reliability, and users who need to trust the output.
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ANONYMISED LOOP DIAGRAM
abstract system shape · 240×180 A confidential agentic system that runs repeated ML experimentation loops: hypothesis, code, training run, scoring, analysis, and next experiment. The hard work is not making the agent act. It is preventing reward hacking, false progress, wasted compute, and unreviewable changes.
The engineering challenge was designing the loop so progress could be trusted.
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ANONYMISED CONSENSUS DIAGRAM
abstract review flow · 240×180 A confidential platform for turning inconsistent human review into audit-grade, model-ready datasets. The system combines weighted consensus, disagreement surfacing, provenance, and QA workflows so the resulting data can be trusted downstream.
The value is not automation for its own sake. It is higher-confidence data production under real operational constraints.
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earlier-shipped
Two decades of shipping production software.
Before the frontier work: document-intelligence and retrieval systems for clients with strict compliance and audit requirements, and twenty years of commerce platforms, payment integrations and web infrastructure. The base layer underneath everything since.
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