AI Implementation Consulting

Because a demo and a production system are different products, and the gap between them is where all the work is. A demo needs to succeed once, on a clean input, with someone knowledgeable driving. A production system has to handle the invoice that was photographed at an angle, the customer who asks three questions in one sentence, the API that times out at 4pm on the last day of the month, and the edge case nobody documented because everyone in the business just knows about it. The pilot gets to 80 per cent in three weeks, and the remaining 20 per cent — error handling, evaluation, integration, monitoring, the human escalation path — is 80 per cent of the actual engineering. Teams that have only ever built demos consistently underestimate it, then run out of budget and enthusiasm at the same moment.

Evaluation harnesses, built before the system goes anywhere near a customer. We construct a test set from your real historical cases — including the messy ones — and score every change against it, so we can tell you the accuracy on your data rather than a vendor benchmark. In production we log every interaction, sample outputs for human review, and alert on the things that actually matter: escalation rate climbing, confidence dropping, latency creeping, cost per transaction drifting. "It seemed fine when we tested it" is not a quality process, and it is the standard most AI deployments are held to.

This is the single most common hard constraint we hit in Australian mid-market businesses, and it is usually an industry vertical package whose vendor has no commercial interest in opening it up. There are several ways through: a supported integration or export you did not know existed, a database-level read where the licence permits it, a file-based exchange on a schedule, or robotic automation over the interface as a last resort — which we treat as a genuine last resort because it is brittle and breaks on UI updates. Occasionally the answer is that the system is the blocker and the honest recommendation is to change the system, not to build an elaborate workaround around it. We would rather tell you that in week one than bill you to build the workaround.

We hand over, and the engagement is structured to make that real. The code lives in your repository under your ownership from day one. The infrastructure runs in your accounts, on your billing, not resold through us. We write the runbook, we train your team on it, and we run a period of supported operation where your people drive and we sit behind them. Some clients then keep us on a light retainer for changes and oversight — see the fractional AI officer arrangement — and plenty do not. Both are fine. A build you cannot maintain without us is a liability we have sold you, not an asset.

A single well-scoped workflow — document extraction, an internal knowledge assistant, an enquiry triage agent — typically runs six to ten weeks from kick-off to production, including the evaluation and handover phases that most quotes quietly omit. Multi-workflow programmes or anything touching a system with a difficult integration surface run longer. We deliberately do not quote two-week AI builds, because a two-week build is a demo, and you would be paying us to create the exact pilot-purgatory problem you hired us to avoid.

Whichever ones fit the problem, and we are not aligned to any of them. We have no reseller agreements, no partner tiers and no commissions to protect, which means model selection is an engineering decision rather than a commercial one. In practice most systems end up using more than one model — a capable one for the hard reasoning step, a small cheap fast one for classification and routing — because that is usually the sensible cost and latency trade-off. We also design for substitution: the model sits behind an interface so you can swap it when something better or cheaper appears, which in this market it will, probably within months.