Stop wasting budget on AI that never ships.
I help Australian operational businesses — in industrial, logistics, infrastructure, and property services — move from AI experimentation to production systems that reduce operational cost and deliver measurable ROI. Structured audits, costed roadmaps, and engineering delivered by someone who has shipped this work inside real businesses.
30-minute scoping call. No commitment. Clear next steps within 7 days.
Built by Michael Minto
15+ years production engineering · Newcastle, NSW
Your path from idea to production
- 1. Audit — Identify highest-ROI opportunity. → ROI model
- 2. Strategy — Costed roadmap with risks and scope. → Implementation plan
- 3. Build — Production AI with monitoring built in. → Deployed system
- 4. Scale — Expand into adjacent workflows. → Ongoing optimisation
years in production data engineering
reduction in manual workflow effort, past delivery
from scoping call to costed audit deliverable
Why most AI projects fail.
The same three problems. Every time.
Endless pilots
Proof-of-concepts that never ship. Months of experimentation with no production outcome — and stakeholder confidence eroding with every update meeting.
No clear ROI
Budget gets allocated to “AI initiatives” without modelling which problem is worth solving first. No commercial case means no executive buy-in.
No path to production
Demos work in isolation but there is no plan for monitoring, failure handling, or integration with real systems. The gap between prototype and production is where most projects die.
Built by someone who has shipped this work before.
Not a sales-led consultancy. Not a recently-rebadged generalist. A senior data and AI engineer offering directly what he has spent fifteen years building inside real businesses.
I’m Michael Minto. For more than fifteen years I have built production-grade data and analytics systems for Australian businesses including Orica, the Hunter Valley Coal Chain Coordinator, and Commercial Collective — operational businesses with real systems, real volumes, and real consequences when things break.
Most recently as a Senior Data Engineer, I designed and shipped data platforms that reduced manual workflow effort by more than 90% and replaced legacy reporting at critical-infrastructure operators. I have now turned that delivery experience toward AI — architecting and deploying a production-grade agentic AI platform with RAG pipelines, distributed execution, monitoring, and governance built in.
Insight Engineered AI is what I built to bring that engineering discipline to mid-market AI delivery. Audit, strategy, build, and ongoing advisory — designed around what I have seen actually break in production, not what looks good in a slide deck.
A structured path from assessment to production.
Four phases. Predictable scope at each step. No mystery about what you are buying.
1. AI Readiness Audit
Fixed-scope assessment that identifies the highest-value AI opportunity in your business, models ROI, maps risks, and produces a clear implementation roadmap.
Output → ROI model + implementation roadmap
2. Strategy & Roadmap
A costed, phased plan with milestones, integration requirements, and a risk register. You know exactly what will be built, when, and at what cost — before any build budget is committed.
Output → Costed delivery roadmap
3. Production Build
End-to-end implementation with monitoring, quality checks, and safe failure handling. Full documentation and handover. Built for production from day one — not retrofitted after the demo.
Output → Deployed, monitored system
4. Ongoing Advisory
Continuous oversight, optimisation, and expansion into adjacent workflows. Systems that maintain performance instead of degrading after handover.
Output → Ongoing optimisation and expansion
Designed for measurable business outcomes.
I focus on the AI work that moves operational metrics — not theoretical capability.
Reduce operational cost
Free up staff capacity by automating repetitive, high-volume processes — in operations, data handling, reporting, or customer workflows. Past delivery has reduced manual effort by 90% or more in equivalent contexts.
Production in weeks, not months
The audit-to-deployment methodology moves from scoping to production in weeks — not the months-long proof-of-concept loops that drain budget and erode stakeholder confidence.
A credible case for leadership
The AI Readiness Audit produces a real ROI model and risk register your executive team can actually evaluate. No hand-waving, no “AI strategy” deck. A document that makes the decision easier.
Built for real business environments.
Every system is designed for production from day one — not retrofitted after the demo.
Production-ready infrastructure
- • Secure, monitored systems with logging and alerting
- • Designed to integrate with your existing tools and data
- • Safe failure handling — systems degrade gracefully, not catastrophically
- • Built on modern Azure cloud infrastructure
- • Full documentation and handover to your team
Operator-led, not sales-led
Most AI consultancies are run by people whose deepest production experience is a demo. I am a senior data engineer who has spent fifteen years building, shipping, and maintaining the systems Australian operational businesses actually run on. I design AI delivery around what I have seen break in real environments — not what looks impressive in a pitch deck.
Based in Newcastle / Lake Macquarie, NSW. Working across Australia and remotely.
Three engagements. One delivery model.
Each tier produces a concrete outcome. Start where the value is — not where a generic consulting menu says you should.
Start here
AI Readiness Audit
- • Identify highest-ROI use case for your business
- • Feasibility and risk assessment
- • ROI model your leadership team can evaluate
- • Implementation roadmap with costed phases
Build
AI System Build
- • End-to-end implementation against the audit roadmap
- • Production deployment with monitoring and failure handling
- • Documentation and handover to your team
- • No throwaway prototypes — built for production from day one
Scale
Ongoing Advisory
- • Continuous optimisation as your systems run
- • Expansion into adjacent workflows once value is proven
- • Performance monitoring and capability building inside your team
- • Strategic input as AI delivery evolves
Stop experimenting. Start shipping.
The AI Readiness Audit starts with a 30-minute scoping call. Within seven days you will have a clear report identifying your highest-value AI opportunity, a realistic ROI model, a risk analysis, and an implementation roadmap your leadership team can evaluate.
No long contracts. No upfront commitment. A concrete deliverable in seven days.