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.

Enhancing AI with Task-Specific Context

The allure of a business hive mind

Imagine if all your business information was held in a single 'hive mind'. This is the vision many have when integrating AI into their operations—an omniscient system capable of knowing and acting upon every facet of the business. The idea is compelling: one central AI that commands a complete grasp of your business, offering precise insights and perfect decisions. But is this really achievable?

In its current state, AI technology struggles to meet these expectations. Attempting to construct a hive mind has exposed significant challenges—information overload, processing delays, and reliability issues are common. Most automation fails when tasked with managing vast, dynamic data sets.

Why hive minds stumble

A hive mind approach assumes AI can comprehend and manage vast arrays of business data seamlessly. In reality, AI systems often hit a wall due to sheer volume and complexity. The problem isn’t just about data processing; it’s about context. Without proper context, AI's ability to interpret and act meaningfully plummets.

Think of it like trying to have a conversation in a foreign language without understanding cultural nuances. Sure, AI can translate words, but it lacks the context needed to grasp the underlying meaning. This gap in understanding causes inefficiencies and errors—a direct hit to a business's productivity and decision-making precision.

A different direction: task-specific context

At hmn.plus, we realized that aiming for a comprehensive hive mind was asking for trouble and developed a more effective approach: providing AI with task-specific context at the moment it needs it. Instead of overwhelming AI with all possible information, we offer a streamlined, relevant set of data points right when required.

This is much like speaking to an expert on a specific point rather than a generalist with tangential knowledge. The expert—our AI—has access to just the information that matters for the task at hand, leading to better outcomes.

How hmn.plus does it

We built workflows that dynamically retrieve relevant information nodes. These aren’t static nor exhaustive—they’re smartly curated lists of data the AI needs for precise decision-making. By avoiding data deluge, we’ve created more 'More human per hour' AI interventions that are both efficient and reliable.

Here's the beauty: these workflows ensure our AI isn't burdened with extraneous data, minimizing error rates and maximizing relevance. It's about being strategic before tactical—ensuring AI's decisions are guided by the right information every time.

Outcome over activity

Choosing task-specific context isn't just about efficiency; it also aligns AI efforts with business outcomes rather than mere activity. This approach ensures that every AI action contributes towards achieving real business goals. The days of AI performing actions just because it can are behind us. Each move is driven by context, leading to meaningful results.

In practice, this means fewer failed automations and more AI interventions that matter. It's about enabling your business to function in its zone of genius, with AI supporting, rather than confounding, its efforts.

Conclusion: smarter AI starts with smarter context

The hive mind is an attractive notion, no doubt, but pragmatism wins in the end. By equipping AI with tailored, task-specific context, you’re enhancing its capabilities without amplifying its flaws. At hmn.plus, this approach has not only increased our efficiency but has also made our AI integrations more reliable and effective. It’s a path worth considering for anyone looking to finetune their AI strategy.