Do I need to know what should be automated?

No. Most people arrive with a feeling rather than a specification — something takes too long, or only one person can do it, or it gets dropped whenever the week is busy. That is enough to start from. If you can point at one job you would gladly stop doing by hand, we can work with that. If you can’t point at one yet, start with the free diagnostic instead: it finds the constraint first, and the workflow follows from it.

What happens with the first workflow?

We build it. You describe the job the way you would explain it to somebody starting on Monday — what comes in, what you would check, what should go out, and what you would never let leave without reading it. That becomes a working workflow, connected to the tools you already use, running on your account. You watch it run on real work before you rely on it. One workflow, start to finish, so what you are judging is a thing that works rather than a proposal for one.

What happens after that?

It runs. Keeping it running is the recurring part — hosting, the models it calls, monitoring, and the fixes for when something upstream changes its mind about a field name. Most people then add a second workflow, and it is usually one they spotted themselves while watching the first one work. There is no bundle to commit to up front. The next workflow is a decision you make when you want it, and not before.

Can my team eventually build workflows themselves?

That is the intended end state, and the training exists for it. Once your team has watched a few workflows get built, the skill turns out to be the describing — knowing what the work actually involves and where the judgment sits — which is the part your team already has. Some businesses would rather we kept building, and that is a legitimate choice rather than a failure. Most want at least one person inside who can change something without picking up the phone.

Do I become dependent on hmn.plus?

It is the right question to ask any vendor, and the honest answer is that you can be for exactly as long as it is useful. Three things are deliberate: the workflows are described in plain language rather than locked in a format only we can read, they run on infrastructure that can sit in your own name, and we teach the building instead of keeping it. Being the only people who can change your systems would be a business model. It is not ours.

Can we eventually own the system?

Yes, and it is an option rather than a destination — plenty of businesses would rather we kept running it, and that is a reasonable thing to want. When you do want it, the workflows, their definitions and the infrastructure transfer to you, and your team keeps the training that makes them usable. We would rather you stayed because staying is worth it.

The first automation is a fixed price, so the first decision is a small one: NZ$500 + GST to map it and get it built. After that, the platform it runs on is NZ$150 + GST a month for each person who builds workflows — everyone else who uses it is free — and that covers hosting, running your workflows, monitoring and support. Workflows you build yourself cost nothing extra; the ones we build are quoted one at a time, on what each actually involves. No annual lock-in, and nothing larger to sign before you have seen something of your own working.

Why not use ChatGPT, Claude, Zapier or n8n?

They answer a different question. Chat tools put something capable on the other end of a conversation, and then nothing happens unless a person types — every session starts again, and what the business knows ends up living in whoever writes the best prompt. Zapier and n8n wire apps to each other; the wiring holds until a field name changes, and it never makes a judgment call. Both leave the work depending on a particular person. What we build has a fixed shape: the same steps in the same order, your rules written in, a record of every action and the reason for it, and a person standing where a person should stand. The thinking inside a step can be as sharp as it needs to be. The path through it does not change because somebody phrased something differently. That is the difference between a tool one person is good at and a system a business runs on.

Where should AI actually be used?

On the judgment inside a step, not on the whole step. Reading an enquiry and working out what kind it is: yes. Drafting a reply in your voice, from your own past replies: yes. Moving money, or telling a client something binding, with nobody looking: no. What we build to is that anything with consequences waits for a person, and everything in front of it does not have to. A faster version of a bad process is still a bad process, which is why deciding where AI belongs happens before anything gets built, and is most of the value.

Execution Data: The Real Product Behind Workflows

Our initial misstep

At hmn.plus, we initially viewed workflows as mere outputs—tools to streamline processes and generate results. But soon, we unearthed a hard truth: the true product wasn't the workflow itself. It was the data these executions generated. Understanding this opened up new avenues for driving business growth.

Introducing the dashboard

The journey began with our dashboard innovation. It encapsulates company-related workflow data, showcasing workflows run, average times, and insights drawn from workflow generation data. This isn’t just about seeing what's been done—it's unraveling patterns and performance, understanding what often goes unnoticed.

Diving into insights

Our insights page is where the magic really happens. With all workflow data stored within your deployment, you can now analyze not just each workflow, but gain insights at the broader business level. For instance, analyze how teams interact with customers or measure the latency in response times. With this data, you can pinpoint delay points between sequential processes, particularly effective when tracking communication workflows.

This new feature allows you to consult an AI, engaging with your data conversationally. By exploring the execution logs, you can locate potential bottlenecks or improvement areas effortlessly. The AI doesn't just observe—it interacts, offering a new dimension to your data analytics.

Unveiling execution logs

We’re enhancing capabilities further with execution logs. Access historical data down to each workflow, examining everything from emails sent to PDFs generated. Such visibility transforms how you approach business workflows, granting you the power to revisit and refine existing processes.

Beyond marketing insights

This latest advance isn’t just about marketing; it applies across all departments. Departments can use these insights to identify who’s triggering workflows and where the workload truly lies. For example, marketing might be initiating many workflows, but the product team might be bearing the greatest execution load, providing a clearer picture of intra-departmental dynamics.

Unveiling potential

With the release of these features, you have more control. Download the release and start exploring the comprehensive insights available. Build and trigger workflows while uncovering which areas of your operations can evolve. These are opportunities to refine and optimize your business strategy.

Feedback is invaluable, and we invite you to interact with this data. Discover what you can analyze and ask critical questions. Our insights aren't static; they’re dynamic, ready for your input and ideas. Let’s shape a more efficient future together.