AI writes the code. You can see exactly what it did, and check it holds up.
Before any code, you approve a plain-language specification as the source of truth. The AI writes only the business logic, on a fixed foundation it can't redesign; every change is tested and checked for regressions; and the full record lands in your own repository.
Security
Scalability
Maintainability
Generation (LLM)
Feedback loop
When AI writes the code, the questions your architects actually ask
Vibe coding is useful when the goal is to reach a working version quickly. Production software needs another guarantee: approved intent, constrained generation, repeatable checks and a record architects can inspect.
Plausible, not correct.
The demo passes. The wrong answer surfaces months later, in the one path nobody thought to check.
Reviewed by no one.
Auditing thousands of lines you didn't write, from a model you can't fully follow, isn't a real control.
Unstable over time.
Every model change quietly breaks something that worked yesterday, a regression tax you only meet in production.
And a better prompt doesn't make them go away.
You can't add more agents to fix this
The popular answer is more agents, bigger context windows, longer runs. It doesn't hold.
The failures persist.
As context grows, agents lose the thread, hallucinate, and miss constraints, and the more they iterate, the heavier and riskier the codebase gets.
The cost explodes.
Swarms burn most of their tokens re-reading context and retrying, not shipping working code, and every parallel run adds cost without adding correctness.
Quality and cost don't come from more agents. They come from structure: a fixed foundation the model can't touch, deterministic checks, and a human in the loop, so the model is spent only on your business logic.
Four things make the output something you can stand behind
Not a promise, a construction. Each one answers a fear above, and each opens up for your architects.
You review a specification, not code you can't
Before any code, Maleus turns the conversation into a validated specification: pages, data, roles, access rules, business logic. A human signs it off as the source of truth, and the code is generated from it.
Your reviewer signs off on something legible, not thousands of lines no one wrote by hand. And the specification stays live, so changes never drift from it.
Specification · Version 1
Source of truthPages
- Request queue
- Approval view
Roles & access
- Submitter
- Approver
Data
- Requests
- Decisions
Business rules
- Validated on submit
- Locked once approved
The technical foundation is in place. The model only adds the business feature layers.
Maleus runs on a reusable, fixed foundation: SSO, encryption, multi-tenant and audit for security; multi-node sync and indexes for scale; the architecture and tests for maintainability. The model only writes the business layer.
So the failure where AI invents its own structure is out of reach by construction, and the model is spent only on your business logic, not on plumbing.
Every iteration is gated, with a human sign-off
Generation runs paired with an automatic feedback loop: static analysis and non-regression tests on every iteration, with a human signing off. When the data model changes, the migration runs and carries your existing data forward.
This is the answer to code that passes once, deploys, and breaks three months later. The loop runs on every change, not once at the end.
Loops automatically with generation, every iteration.
Everything the AI did is on the record
You get all of the code and the complete build-session history, every action every specialist took, open to your own developers, delivered into your own Git repository on a standard stack: Node, NestJS, Angular, PostgreSQL.
What the AI did is no longer a hope to verify: it's inspectable, any developer can take it forward, and there's no proprietary runtime to be trapped inside.
Quality checked on every version
You don't take our word for it. Maleus scores every build on quality, maintainability, and test coverage, and the report is open to your own engineers.
The same scorecard a senior team would run, on every version, before it ships.
See the architecture and the checks on your own use case.
Book a demo and we'll walk your team through the specifications, the fixed foundation, and the audit trail, on a real internal-software scenario.
Built by the team behind a widely-used open-source backend and real-time data platform.