The foundation for AI transformation

Structify is the foundation for AI transformation in complex and regulated industries, starting with US mid-market industrial manufacturing.

What is an AI transformation?

AI transformation is the process of moving units of value and work onto AI. It is not a pilot, and it is not a chatbot.

The unit is an AI business application. A quoting process, a control plan, or a monthly report moved onto AI and kept there. We approach a transformation this way, automating one portion at a time.

However, applications are insufficient for a complete transformation. Transformation requires finding the use cases worth automating, building them, maintaining them, and moving the people whose work changed over to the new way of doing it. It is the continual job of keeping the company moving as smoothly and efficiently as possible.

It would be nice if this could be tied to one specific, marketable use case. It cannot. Every business works differently, so its units of value are different, and every transformation is different.

Why do you need a foundation?

Before building a house, it's best to lay a foundation. If you only want a basement fort or a backyard tent, it's faster to go without a foundation, but building a house is different. It's something you will live in. It's something you will depend on. It's expensive, and it cannot fail. For such projects, it's worth taking the time to lay a foundation, do the plumbing properly, and have experts do the wiring.

At best, many existing AI applications are tents and forts. At worst, they are unstable straw houses vulnerable to a big gust of wind or a big bad wolf. Thrown together quickly, they will even more quickly fall apart.

A company that undergoes a complete AI transformation does not end up with just one application. It ends up with dozens, then hundreds. AI applications are form-fit, ideal for intra-business virality. However, that virality leads to dependence on an application that is increasingly unstable. The logic of each one lives in the head of whoever built it - a clear key-man risk. Without one way to organize and deploy them, with shared permissions and audit logs, you get a burst of wins and then a slow rot as nobody remembers how any of it works.

Our goal at Structify is to be a foundation countering that rot. We are stable to build on and easy to maintain into the future. We make the transformation scalable. We make checks easy, keeping accuracy high and mistakes known.

Foundation Components

A lot of components can enable you to build more successful AI business applications more quickly. Some of you may have heard of semantic layers or ontologies. I like to think of these as a data-level and a business-level understanding. Historically, much of how a business worked was conveyed through oral tradition. AI cannot work from oral tradition. Both understandings now have to be transcribed and kept current.

There are also layers that we don't yet, if ever, trust an AI to write on its own. Authentication, permissions, audit logs, and security. You can vibe code these in-house, but then the liability falls on you if they're wrong.

Why does the foundation pay for itself?

If you pay for the foundation, everything else becomes cheaper.

You don't need a SaaS application that addresses a million businesses. You need something much simpler that addresses just yours. Every business is different, so I often say that form-fit, custom applications are the best, except in two ways. They are terrible to implement upfront, and they are terrible to maintain. The foundation takes both costs down, making implementation lower cost and faster.

It does that by being the same every time. Once there is a common foundation and a common way of building, the blocks become reusable. The parts that come up again and again go into a component library, and the applications that come up again and again go into a catalog, so each new project starts from a template instead of a blank page. Every one is deployed the same way, with the same authentication, permissions, and audit logs underneath. The applications are form-fit. The way we build them is not. That repetition is what makes custom cheap, and it is why the applications keep working and are maintainable after the person who built them moves on.

The applications are form-fit. The way we build them is not.

Where are we starting?

The foundation matters most in places where making mistakes isn't an option. The best example is regulated industries. We have had successes across them - in manufacturing, distribution, financial institutions, and government.

We thrive in regulated industries. If you need to stay compliant with HIPAA, CMMC Level 2, GDPR, or Reg S-P, AI is still useful to you, but you have to use it differently. You cannot throw PII or CUI into ChatGPT like your unregulated colleagues. You can play around with AI, but some things you need to be very sure of. Authentication has to be secure. Audit logs have to be intact. Data has to be safe. Overall, you have to make sure you are compliant. When more than half your business depends on that standard, you cannot afford to risk a backyard tent.

We thrive in complex industries. Beyond the guarantees that compliance requires, a foundation should accelerate your timeline. It should give you the common building blocks you'll need, and it should make your AI better than it would be on its own. Our goal is always to be a value add on top of what the models are capable of on their own. That delta is biggest on complex problems.

At the moment, we are focused primarily on US mid-market industrial manufacturers. The push to onshore more of our manufacturing is working in our favor. We can help most where it has historically been hard to recruit IT and software talent, and that is usually the non-OEM, industrial, mid-market shop.

Conclusion

The systems that hold a company's core records and transactions will remain. What will change is the application layer through which much of the company's work gets done. Standardized SaaS workflows will increasingly give way to form-fit AI applications built around the way each business actually operates. People want something that addresses their needs, not the average person's needs. That future only works if the applications and automations sit on something that holds. A tent is fine for a pilot program. A company that runs on a hundred AI applications needs a house, and the foundation is what makes the hundredth one as cheap to build and as safe to depend on as the first. The businesses that need this first are the ones where being wrong is not an option and hiring engineers is hard. Structify is the foundation for AI transformation in complex and regulated industries, starting with US mid-market industrial manufacturing.