Six years ago, we started J4RVIS in Sydney with a small team of Salesforce professionals and no appetite for slideware. It worked. Summit Partner, Deloitte Fast 50 twice, more than 100 people across Australia and the Philippines. We managed through Covid and now navigate through the fastest moving technology evolution we have seen: Artificial Intelligence. We always embraced innovation and pivots to best serve our customers and have transformed J4RVIS from a boutique Salesforce and MuleSoft partner into an organisation that solves the toughest challenges across business systems, data, integration, and artificial intelligence. To help our customers transform, we also looked at us as an organisation and we rebuilt what we build instead.

That started with how we work. Every consultant here works with AI, which is easy to say and means little on its own: a consultant with a chat window is still guessing. What matters is what sits around the model. Ours is a delivery harness carrying our own accelerators, patterns, and industry IP, with the guidance and controls built in, so an engineer starts from what we already know about the problem rather than a blank prompt. A generic coding agent starts from neither, and so does a platform's own.

Governance is the point of it. No frontier model knows your estate, what good looks like in your business, or which pattern applies to the problem in front of it. Our engineers do, and the harness puts that context and those controls in front of them rather than leaving it to a prompt. The model does more of the work; the engineer makes more of the decisions, and signs off on the ones that ship. That is also where consistency comes from, because every engagement starts from the same patterns and the same standard.

Most enterprises we meet skipped the data foundation years ago, and every agent they try to put into production eventually breaks on it.

Pascal and I called this early. J4RVIS started as an integration partner before it was a Salesforce partner, because the connection layer was clearly where the value would sit. What we did not predict was how much harder that layer would get. Integration used to mean moving a record from one system to another, on a schedule or in real time. Now it means integrating against data lakes, at volumes we have not dealt with before and growing faster because the systems themselves keep generating more. The work is in merging sources into data assets that actually mean something, and indexing them properly so an agent can find what it needs without reading the whole archive. An agent that reads everything to answer one question is expensive every time it runs, and that cost lands on you rather than on the demo.

The layer above that is newer still. APIs are becoming MCP connectors, the Model Context Protocol interface an agent uses to reach a system, so every integration decision is now an agent decision too. That opens a much broader conversation than integration ever was: how you orchestrate agents, as well as how you administer and integrate the data underneath them.

That is why the service line here is Data and Integration rather than integration alone. What began as wiring systems together has become a data business: the platform underneath is as much of the job as the pipes into it, and agent orchestration increasingly sits inside those layers, which changes how whole business systems come together.

It also means watching what the estate costs to run. Token and credit consumption is a live commercial exposure once agents are in production, so we monitor it the way we monitor anything else that runs, and you see it before it shows up on an invoice. Work reaching that far does not belong buried inside someone else's project, so it has its own leader and its own accountability.

That is true of every service line here. We stopped organising around practices and started organising around outcomes, so each one has a single person accountable for it: Diego Mogollon on Artificial Intelligence, Carlos Langle on Data and Integration, David Woo on Business Systems, Mohit Bajaj on Platform Success, and Shibu Keloth on Strategy. I, Jannis, focus on where the firm is headed, while Pascal Uerlings and Curtis Williams lead the commercial engine that gets it there.

Ask any partner what an agent does to their revenue. The answer tells you more than their capability deck. For us, AI opened up commercial models that tie what we charge more closely to whether you get the benefit.

Fixed price is the default: a costed plan, a named owner, and something in production inside the first quarter, agreed up front regardless of headcount.

Forward deployed engineers sit inside your operation instead of working from a specification. Nothing agentic is finished at go-live, so the people who built it stay close to the work while it settles into the business.

Outcome-based pricing is where we are heading. We invoice against whether the agreed result landed rather than the hours it took, which puts our fee on the same side of the table as your business case.

Where we commit to an outcome, we agree what success looks like and put a number on it before a single agent gets built, then carry the risk that used to sit with you. If it does not work, that is our loss to absorb, not a bill we still send.

Four client stories on the new site carry the numbers and the client names: 330% growth in advertiser accounts at Southern Cross Austereo, lead response cut from 16 minutes to 6 at hipages, 35% fewer agent-handled calls at TasWater, 68% more same-day verifications at Pay.com.au. None of it is a promise. It is all running today.

If you are evaluating a partner who says they are AI-native, there are four questions to sort the field fast.

A partner worth hiring answers all four in the first meeting, with names and a reference you can call.

Four client stories on the new site carry the numbers and the client names: 330% growth in advertiser accounts at Southern Cross Austereo, lead response cut from 16 minutes to 6 at hipages, 35% fewer agent-handled calls at TasWater, 68% more same-day verifications at Pay.com.au. None of it is a promise. It is all running today.

If you are evaluating a partner who says they are AI-native, there are four questions to sort the field fast.