2x More Reliable, 40–50x Cheaper – and Every Competitor Has to Rebuild

01 /The Concept

INXM was founded last year and unveiled its platform this week, simultaneously announcing its first €5.7 million raise.

Its enterprise AI platform runs under the banner of "AI that reliably finalizes workflows" – a phrase that earns unpacking.

The generalized process looks like this:

- A user describes in plain language what they need the AI to do.

- Based on that, the INXM platform generates a step-by-step execution plan consisting of specific technical actions – for example, extract data from a document, match it against a database, transform the data in specified ways, and write the output to another system.

- A human reviews and approves the plan.

- Upon approval, the platform executes the steps – calling the necessary third-party procedures or APIs.

- A detailed log of all steps and outcomes is stored for auditing and sent to the user.

Naturally, once approved, the same plan can be executed repeatedly and automatically – without requiring manual approval each time.

The key insight: AI is only used to *generate* plans. The plans themselves are executed without AI.

In plain terms: AI figures out what needs to be done, then INXM executes it cheaply and reliably as many times as needed – far cheaper and more reliably than if AI were re-invoked every time.

02 /Why It Matters

INXM builds on the concept of "Compiled AI," a framework that appeared in a published paper in April – which the startup has already implemented.

AI's fundamental liability is hallucination. Even on identical inputs, AI models occasionally produce incorrect outputs they would have gotten right before. The hallucination rate is high enough that direct AI involvement in repetitive, reliable business processes is simply unacceptable.

The concept's proposed solution: use AI only at the plan-generation stage, then automatically convert ("compile") that plan into a conventional executable program. Programs, by definition, produce the same output for the same input every time.

This approach delivers 96% accuracy. The remaining 4% are not execution errors – they're plan errors: cases where the generated program produces wrong results because it received inputs it wasn't designed to handle.

The fix in those cases is either pre-processing the input data to match what the plan expects, or regenerating the plan to account for that class of input.

The second advantage is cost. Tokens are spent only on plan generation – not on execution. On a real-world task set, pre-compiling plans reduced the cost of executing 1 million operations by 40–50x compared to invoking AI models continuously during execution.

In practice, the right approach mixes both methods – use AI directly for tasks that genuinely require novel reasoning each time, and compile AI for anything that needs to be repeated reliably at scale.

It's also worth noting that "compiled AI" doesn't mean removing AI from recurring workflows entirely – in some cases that's impossible. Document recognition, for instance, inherently requires live AI inference.

But in those cases, the program should wrap the AI tightly with validation logic – forcing it to work in the most constrained, predictable way possible and strictly checking outputs. If errors occur, the AI call is retried with a modified prompt. If errors persist, the system alerts a human to clean the input or regenerate the plan.

This approach has a precedent: a Y Combinator graduate, Rima ([related review](/review/izjashhnaja-biznes-model)), applies a similar philosophy in its AI accounting platform. Their framing: "the biggest lie of the AI revolution" is the promise that AI agents will soon do everything.

The reality is that AI hallucinates, always has, and always will – it's structural, not a bug to be fixed. Nobody wants accounting software that gets the balance sheet right 95% of the time.

AI is a mediocre accountant but an excellent programmer. So: explain the accounting logic to the AI, let it generate the program that implements that logic, verify the program works correctly across edge cases – and then use the program, not the AI, in production. That's exactly how Rima operates.

03 /Opportunities

In a year or two, the current practice of using AI directly for repetitive tasks will look like driving a nail with a microscope. Repetition and consistency are precisely what defines a business process – that's the whole point of having them.

But that gap of one to two years is a meaningful head start for founders who build AI automation platforms on the Compiled AI model right now – demonstrating an order-of-magnitude advantage in cost and reliability over the conventional approach.

Although given current development velocities, that window may be shorter than it sounds. Which means the right move is to move fast.

So: which domain could you build a Compiled AI automation platform for – fast enough to matter?