Getting clarity with AI is a matter of alignment.
AI can generate more code than ever before.
That is no longer the problem.
The real challenge is making sure AI understands your domain, follows your rules, and produces results that remain manageable, reviewable and trustworthy.
30 minutes to discover where we can speed up
When code becomes cheap, verification becomes expensive.
Generative AI has changed the economics of software development. Producing another implementation is becoming almost effortless.
Understanding whether that implementation is correct, however, still requires knowledge of your architecture, your business rules, your constraints and your intent.
More generated code therefore does not automatically create more engineering capacity. It can just create more things to review.
That is where domain models matter.
Implementation absorbs the effort. Review is proportional to what a team could write.
Implementation is nearly free. Assurance is the constraint, and it does not scale with tokens.
Give AI a model of your domain.
Domain models give AI something more valuable than more context: structure.
They define the concepts, relationships, constraints and rules that matter in your organisation. Instead of asking an AI agent to reconstruct your domain from documentation, source code, tickets and prompts, you give it an explicit representation of how your domain works.
That changes the role of AI. It no longer has to invent the structure. It can work within it.
Example of how we do this in JetBrains MPS →Context tells AI more. Models tell AI what things mean.
A larger context window can expose an agent to more information. It does not make that information authoritative.
A domain model does. It provides a shared semantic layer between your organisation, your software and your AI agents. The same concepts and rules can guide:
AI must infer the rules
The rules are explicit
Context provides information. Models provide meaning.
One domain. One source of meaning. Many AI capabilities.
Make the model enforceable.
AI handles the variation. Your models define what is allowed.
A domain-specific language turns domain knowledge into something an agent cannot simply reason around. Its grammar, type system, constraints and validators define the space in which the agent can operate.
That changes the development loop. Many invalid states become impossible to express. Other violations are rejected immediately by types, constraints or validators. And because those failures are explicit and machine-readable, the agent can correct itself before a human has to inspect the result.
Move verification closer to intent.
Every translation introduces another place where meaning can drift.
Domain experts
With executable domain models, the people who understand the rules can inspect the artifact that actually defines them.
Engineers
No longer validating every generated implementation line by line. Instead, they build and maintain the language, constraints, validators and generators that apply those rules repeatedly.
Auditors
An explicit, versioned and diffable representation of what the system was supposed to do.
TaxRule {
eligibility: ...
rate: ...
exception: ...
}
Review the rule, not every consequence of the rule.
The AI agent works inside the engineering system.
The model is not another document beside the software. It becomes part of the system that produces it.
The agent can move quickly. The engineering system decides where it is allowed to go.
Domain model — defines the concepts and intent.
DSL & constraints — define what can be expressed.
AI agent — explores solutions inside that space.
Validators — reject invalid results immediately.
Generators & transformations — produce deterministic technical artifacts where appropriate.
Humans — review domain intent, exceptions and meaningful changes.
Stop putting your domain knowledge in prompts.
Prompts are useful instructions. They are a poor place to keep critical engineering knowledge.
Your most important domain rules should not depend on whether an agent happens to interpret a paragraph correctly today. They should live in explicit models, constraints and transformations that can be inspected, versioned, tested and reused.
guide behaviour
define reality
AI also changes the economics of modelling.
AI is making the guardrails it needs cheaper to build.
There is an important second-order effect.
Domain-specific modelling has always offered leverage, but creating languages, editors, transformations and integrations required specialised engineering effort. AI is reducing that cost dramatically. It can accelerate the repetitive parts of language engineering, tooling and integration while the formal model still provides the structure that keeps the resulting automation under control.
That makes domain-specific modelling relevant far beyond the organisations that could previously justify large modelling programmes.
A genie built into your engineering environment.
There is no F1RE platform that needs to remain between you and your software.
We build the modelling layer inside your repositories, CI and infrastructure. Depending on the environment, that may include technologies such as JetBrains MPS, Xtext, LionWeb and the modelling infrastructure you already run.
The language, validators, generators, integrations and test harnesses remain yours.
No proprietary runtime. No artificial lock-in. No imported domain IP.
The goal is not to make F1RE indispensable. The goal is to make your domain knowledge operational.
We build where the constraints already live.
Greenfield AI demos are easy. Existing engineering organisations are not. Their rules are distributed across codebases, generators, modelling environments, legacy systems, documentation and years of decisions.
That is precisely where model-driven engineering experience matters.
F1RE works with domain-specific languages, language workbenches, model transformations and generators across modern and long-lived engineering environments. Our team combines 51+ years of DSL and model-driven engineering experience, including expertise in DSL semantics, verified compilation and program transformation, and contributions to MPS-extensions, mbeddr.core and LionWeb.
Agentic AI tooling is part of our daily engineering practice — not a separate AI offering layered on top.
We measure whether the constraint layer actually helps.
On our own tooling we compared the same AI model performing the same task through two different interfaces. One exposed a generic modelling environment through roughly 35 tools. The other exposed a smaller, domain-specific and constraint-aware tool surface.
Across three runs each, the domain-specific interface required fewer steps, less wall-clock time and fewer tokens.
More importantly, the experiment also showed us where the boundary still was. One undocumented domain constraint was violated through both interfaces. The validator caught it and forced a retry, but neither interface prevented the attempt.
That is exactly the distinction that matters: giving AI access to your tools is not the same as giving AI an explicit model of your domain.
Start with one real subsystem.
Not an AI roadmap. Not a capability demo.
Choose one rule set, configuration surface or integration where domain knowledge is real and change is currently painful. A bounded first engagement gives you:
Then you decide. If the approach works, we expand it and enable your engineers to extend the system themselves. If it does not, you stop. You keep the models, source, integrations and what your team learned.
