In the last few years, we’ve been living in a flood of AI hype. Every three months, there’s a new innovation that seems like it could change business forever. On the other hand, with the massive investments being made and ever-growing promises about the next model, some are starting to think that AI is all hype. We don’t think it is. But the real business value is only just arriving.

Large Language Models are an amazing technology. Our feeds are full of impressive demoes of what we can do with them. Chatbots were amazing when they arrived a couple of years ago. But the value for businesses didn’t really materialize. It turns out hallucinations make them unreliable.

The Claude Code and Claw-like agents were shockingly futuristic at first. But just like the chatbots, the demo did not deliver. It turned out they require so much maintenance, and so many tokens, that the ROI is still low.

The biggest business impact I’ve seen is in software development. Why? Because code can be tested. Unlike the wall of slop you saw on LinkedIn this morning, your team is shipping working code to production. Generating code has become so valuable that some companies are bragging about developers burning through tokens to equal their salary. Code generation is already useful, but expensive.

Compiled AI resolves all of this, making AI into a real business case. Hallucinations are kept out of production use cases, maintenance is low and simple enough for the average user to handle, and the cost structure is much better. The term Compiled AI was coined in this paper released in April (together with some very interesting benchmarks). But at INXM we’ve been hard at work on this for over a year.

At it’s core Compiled AI means you use LLMs to generate deterministic, enterprise-ready, code. You then run the code to achieve your outcome. This gives you the flexibility of natural language from AI models, but the testability of deterministic code.

It’s also a lot cheaper.

This is crucial step forward since a business in practice is a handful of reliable processes. If we had to rely on the 98% probability of LLMs, most of us wouldn’t be in business. The risk profile this probability creates would make delivery sporadic even in small businesses. In enterprise where processes are more complex it would be unusable.

Each step in a business process multiplies this possible error rate from an LLM, until eventually it hits the ceiling of 100%. This is partly why so many AI pilots fail.

We knew this when we started INXM, but we didn’t have a term to describe the solution, until now. Compiled AI does not have these problems.

Our Process Execution Engine, called the Orchestrator, uses Compiled AI to do work more reliably than chatbots or agents, but it’s also more reliably than legacy automation systems. Using the strengths of AI to make Compiled AI processes much less fragile to changes.

Compiled AI also uses a lot less tokens than basic LLM based approaches. How much depends on the use case, in the paper that coined the term they created a benchmark that showed a 90% reduction. This is a remarkable cost reduction. Especially in Europe where we are severely constrained by data center build out. We believe that using 90% less tokens resolves the token bottle neck in Europe, keeping your data secure.

Every Business runs on reliable processes. We need reliable and auditable transactions through all our systems of record. Probabilistic tools don’t provide this, Compiled Ai does.

The larger the organization, the more flexibility their processes require. Our Compiled AI lets humans make key decisions and adapt to new context, which means we don’t need to rely on IT to maintain fragile automations.

We believe Compiled AI changes the business case completely. AI is now enterprise ready.