AI accelerates development, Axini keeps it manageable

AI assistants and coding agents create code, tests and specifications faster than ever. But AI still makes mistakes. Like driving, speed without control leads to crashes. The Axini Modeling Platform (AMP) gives you that control, so you can deliver reliable software faster with AI.

5×

Reported acceleration in teams programming with AI

2×

Reported increase in error counts when using AI

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Two questions that must always be answered

In an ideal world, AI makes no mistakes. This means the AI perfectly understands what needs to be built and makes no technical errors. Current AI cannot do this alone, but with Axini's help it can: we ask these two fundamental questions:

Did we build the right thing?

A specification in natural language leaves room for interpretation. A model captures the intended behaviour unambiguously, so that customer, developer and AI all consult the same source.

Did we build it right?

The Axini platform thoroughly tests the application for correctness. When AI and Axini work together, we can precisely verify that the system is built correctly — and that the right thing is built.

Illustration of human-AI collaboration around a formal model

What happens in practice

In organisations that adopt AI broadly in software development, the following patterns recur.

Volume grows faster than review capacity

AI produces more code, tests and documentation than a team can manually review. Without automated verification, the actual quality gate shifts to the production environment.

Defects shift to later stages

Time saved during build is often given back during integration, acceptance or production. Without early, formal verification, root causes are hard to trace back to their source.

They are not unique to AI, but the higher development velocity makes them grow faster.

How Axini applies AI

As AI writes a larger share of the code, the Axini platform tests far more thoroughly than manual or script-based testing allows: tests are derived systematically from the model, covering combinations and sequences a person would rarely think of. Five concrete applications in which a model serves as the bridge between specification, AI-generated implementation and independent verification:

Models as shared reference

The desired behaviour is captured in a formal model. It provides an unambiguous and controllable basis for both reviewers and AI-coding agents.

Generated tests

Test cases are automatically derived from the model and systematically cover the specified behaviour. This makes test selection deterministic and repeatable, rather than dependent on prompt variations.

Verification in the agentic loop

After each iteration, a coding agent receives feedback from the same model-based tests. Deviations become visible early and can be traced directly to the specified behaviour.

Modelling legacy systems

For migrations or rebuilds, the behaviour of the existing system is captured in a model. The new system, AI-built or not, is then tested against that reference.

Monitoring

For systems with AI components in production, the model can serve as a passive reference. It directly signals when observed behaviour falls outside the specified boundaries.

What a typical engagement looks like

A typical engagement has four phases. The sequence is iterative, not linear.

Exploration

We map the system, the existing specifications and the current test coverage. The goal is to understand where the risk sits and where a model adds the most value.

Modelling

Intended behaviour is captured together with domain experts. The act of modelling itself makes implicit assumptions explicit and often yields insights of its own.

Generation and execution

The platform derives test cases from the model and runs them against the system. Results trace back to specific model transitions.

Maintenance

When code, prompts or the system change, the model grows along. It remains the reference against which every next version is checked.