Research & Field Notes
Applied AI R&D, engineering practice, and lessons from building intelligent systems — written by the MarcTech team.
Small-Model Economics: Why the Frontier Model Isn't Always the Right Call
When a smaller model beats the frontier model in production. Cost, latency and failure-mode trade-offs from applied AI R&D in Australia, finance to voice.
CPS 230 When Your Service Provider Is an AI System
APRA's operational risk standard never mentions artificial intelligence. It doesn't need to. If your AI sits inside a bank's critical operation, the obligations arrive through the contract — and the transition period has already expired.
AI and the Best Interests Duty: Where Automation Stops in Mortgage Broking
No Australian law addresses AI use by mortgage brokers. That doesn't mean the question is open — the best interests duty already answers most of it, and a privacy deadline in December closes the rest.
Prompt to Production: Shipping AI Features Without Breaking Things
A prompt that works in a playground is not a shipped feature. Here's the internal pipeline we use to take AI from a working prompt to something safe to run in production.
When Not to Use AI: A Decision Framework
The discipline that separates good AI engineering from hype is knowing when not to use it. A practical framework for deciding when a model is the wrong tool for the job.
The Integration Layer: Turning Fragmented Systems Into One Intelligence Surface
Most business AI value isn't blocked by the model. It's blocked by data trapped in disconnected systems. The unglamorous integration layer is where intelligence becomes possible.
Evaluating Language Models for Regulated Work: Accuracy, Auditability, Cost
In finance and professional services you can't ship an AI feature on vibes. Evaluating models for regulated work means measuring accuracy, auditability and cost as one system.
Knowing When to Hand Off: Designing the Escalation Path in an AI System
The hardest decision in an AI product isn't what the system does — it's when it should stop and get a human. Designing that escalation path well is where trust is built.
Grounding Language Models on Proprietary Data: Retrieval That Doesn't Drift
Language models hallucinate on internal data because they were never trained on it. Grounding them reliably is a retrieval problem, not a model problem. Here's how we approach it.
Applied AI R&D: Shipping Production Intelligence Without a Research Lab
Most Australian firms don't need a research lab to do real AI R&D. They need a disciplined loop that turns open questions into shipped systems. Here's the applied approach we use.
The Last 200 Milliseconds: Engineering Latency in Real-Time Voice AI
A natural phone conversation lives or dies on response time. Here's how we engineer the latency budget for a real-time voice AI system — where the milliseconds go, and how to win the ones that matter.