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.

Claude AI for entrepreneurs: How to harness its power

The promise and pitfalls of Claude AI

Entrepreneurs and business owners are rightfully excited about the possibilities Claude AI offers. It’s like discovering a tool that can magically handle tasks once thought to require human intervention. The promise? Improved efficiency and a broader reach without the heavy lifting. The reality? Many still grapple with how to harness these capabilities effectively.

Most businesses mistake using LLMs, particularly Claude AI, for task orchestration. This approach typically leads to inconsistency and shortcuts that quickly erode as the solution scales. We’ve experienced this firsthand at hmn.plus. Let’s dive into why this happens and how we’ve addressed these pain points.

Why relying on LLMs for orchestration is problematic

LLMs are undoubtedly impressive. They handle language processing tasks with fluidity, but task and workflow execution require a different level of precision and reliability. Relying on LLMs for such tasks often means sacrificing these attributes. Here’s why:

Inconsistency: LLMs, while sophisticated, still produce variable results. This inconsistency can seem minor initially but quickly scales into a critical issue.
Security risks: Delegating core business process execution to an AI means handing over sensitive data which, without proper safeguards, increases risk exposure.
Transparency: The ‘black-box’ nature of AI decisions can make it tough to audit processes fully. This is not just a regulatory concern but a business liability.
Shortcuts and workaround: LLMs tend to find path-of-least-resistance solutions, which might introduce unseen flaws into your operations.

Scripted orchestration: The solution

At hmn.plus, we studied these challenges and sought an alternative. Enter iGentico S—a model that uses scripts for workflow orchestration, not LLMs. This hybrid approach allows us to harness the strengths of AI where it’s most beneficial while maintaining control over task execution. Here’s what we found:

Consistency: Scripts provide a predictable framework that ensures uniform task execution, eliminating variance in results.
Security: By defining precise protocols within scripts, we maintain tighter control over data and access.
Scalability: As workloads increase, scripts can be scaled precisely, offering reliable performance without the bloat that AI-driven orchestration might introduce.
Transparency: Scripts are inherently transparent. You can easily audit processes and ensure compliance, providing peace of mind for you and your stakeholders.

Where AI shines and scripts don't

The key lesson is understanding where AI excels and where traditional frameworks outperform. AI is unparalleled for natural language processing, predictive insights, and data analysis. For task execution and workflow orchestration, scripts offer a structured, reliable approach.

By aligning AI with its strengths and combining it with the precision of scripts, you achieve a synergy that bolsters your business capabilities while minimizing liabilities. In discovering Claude AI, we tapped into a resourceful ally, but our journey taught us to harness its power thoughtfully and strategically.

Conclusion: Orchestrating for success

The discovery of Claude AI can revolutionize your business, but only if you're discerning about its application. Lean into AI for what it does best but retain control of your processes with scripted orchestration. This combination creates a robust, scalable, and secure foundation, giving your business the agility to meet today's demands and tomorrow's challenges.