Interview with Almaz Khabibullin: Building the First AI-Native Platform for Live Technical Documentation
Software developer, Co-Founder, CRUDERRA Inc
Q: Mr. Khabibullin, as the lead author of the “CRUDERRA” software, you’ve launched a new generation of developer tooling. What is the core problem you’re solving?
Author: Larisa Safina
Syddansk Universitet, Ph.D., Computer Science
15/03/24
Author: Larisa Safina
Syddansk Universitet, Ph.D., Computer Science
15/03/24
A: The disconnect between code and documentation is one of the oldest, most expensive problems in software engineering. Teams spend 30–40% of their time reconciling outdated diagrams, inconsistent API specs, and missing architecture descriptions. This leads to bugs, security gaps, onboarding delays, and failed releases.
Traditional tools treat documentation as static text—separate from the code lifecycle. We reject that. CRUDERRA is the first platform that treats technical documentation as live, executable, AI-synthesized output of the code itself.
Q: How does your approach differ from tools like Swagger, Confluence, or even newer AI wrappers?
A: Most tools are either manual (Confluence) or passive (Swagger). They require developers to write or annotate. CRUDERRA is active and autonomous.
Our system performs real-time reverse engineering of source code—Java, Python, Go, .NET, even COBOL—and synthesizes a living, interactive architecture model that updates on every Git commit.
Key innovations:
Q: You mentioned COBOL. Why is legacy modernization relevant to your work?
A: Because the problem is most acute in mission-critical systems—banks, insurers, governments—running on decades-old COBOL. These systems have no documentation, or it’s 20 years outdated. Rewriting is too risky; documenting manually takes years.
We’re currently in advanced talks with NEC Corporation following our selection for their NEC-X Startup Accelerator. NEC has thousands of COBOL-based systems they need to modernize without business disruption.
Our platform scans COBOL codebases, reconstructs transaction flows, data models, and dependencies, and generates modern, interactive documentation in seconds—something that would take a team of engineers months or years.
This isn’t just convenience—it’s business continuity.
Q: Is your AI model based on public LLMs like GPT or Llama?
A: No. We do not use off-the-shelf LLMs for core architecture synthesis. Public models lack precision, determinism, and domain grounding. They hallucinate. That’s unacceptable when you’re documenting a banking transaction flow.
We built a specialized hybrid engine:
Q: How does this work reflect your broader contribution to computer science?
A: My career has been driven by one question: “How do we make complex systems legible?”
In CRUDERRA, we’ve formalized a new paradigm: Documentation-as-Code (DocOps) as a real-time, AI-mediated contract between intent and implementation.
This bridges the gap between system design (traditionally done in Figma, Lucidchart, or whiteboards) and code reality (in GitHub, GitLab). Our platform closes the loop:
Q: What is the status of your intellectual property and adoption?
A: We hold patent for the core AI engine, with Rinat Khabibullin and me listed as lead authors. The platform is already in active technical collaboration with NEC Corporation to modernize legacy COBOL systems used in critical banking infrastructure—where accurate, real-time documentation is a regulatory necessity.
In parallel, we are in advanced discussions with engineering teams at multiple Fortune 500 companies in the U.S. and Europe to integrate CRUDERRA into their internal developer platforms. These organizations face the same challenge: documentation that lags behind code creates security, compliance, and scalability risks—and our solution closes that gap automatically.
This real-world adoption by global technology leaders confirms that CRUDERRA is not just a prototype, but a production-grade platform solving a universal engineering problem.
Traditional tools treat documentation as static text—separate from the code lifecycle. We reject that. CRUDERRA is the first platform that treats technical documentation as live, executable, AI-synthesized output of the code itself.
Q: How does your approach differ from tools like Swagger, Confluence, or even newer AI wrappers?
A: Most tools are either manual (Confluence) or passive (Swagger). They require developers to write or annotate. CRUDERRA is active and autonomous.
Our system performs real-time reverse engineering of source code—Java, Python, Go, .NET, even COBOL—and synthesizes a living, interactive architecture model that updates on every Git commit.
Key innovations:
- Proprietary AI engine trained on RFCs, IEEE standards, and millions of open-source commits to generate human-readable narratives from abstract syntax trees (ASTs).
- Live C4/DFC diagrams that are not images, but queryable, versioned, and testable models.
- Design Doc Generator: an AI co-pilot for system architects that drafts full technical specifications from early requirements—and binds them to implementation via code traceability.
- Zero manual writing: developers never write docs. They only review and refine AI output.
Q: You mentioned COBOL. Why is legacy modernization relevant to your work?
A: Because the problem is most acute in mission-critical systems—banks, insurers, governments—running on decades-old COBOL. These systems have no documentation, or it’s 20 years outdated. Rewriting is too risky; documenting manually takes years.
We’re currently in advanced talks with NEC Corporation following our selection for their NEC-X Startup Accelerator. NEC has thousands of COBOL-based systems they need to modernize without business disruption.
Our platform scans COBOL codebases, reconstructs transaction flows, data models, and dependencies, and generates modern, interactive documentation in seconds—something that would take a team of engineers months or years.
This isn’t just convenience—it’s business continuity.
Q: Is your AI model based on public LLMs like GPT or Llama?
A: No. We do not use off-the-shelf LLMs for core architecture synthesis. Public models lack precision, determinism, and domain grounding. They hallucinate. That’s unacceptable when you’re documenting a banking transaction flow.
We built a specialized hybrid engine:
- Symbolic AI for parsing, AST traversal, and formal verification;
- Fine-tuned encoder-decoder transformers trained exclusively on structured technical corpora (RFCs, API specs, cleanroom documentation);
- Rule-based validators that enforce consistency between code state and documentation claims.
Q: How does this work reflect your broader contribution to computer science?
A: My career has been driven by one question: “How do we make complex systems legible?”
In CRUDERRA, we’ve formalized a new paradigm: Documentation-as-Code (DocOps) as a real-time, AI-mediated contract between intent and implementation.
This bridges the gap between system design (traditionally done in Figma, Lucidchart, or whiteboards) and code reality (in GitHub, GitLab). Our platform closes the loop:
- Architect writes a high-level spec →
- AI generates a traceable design doc →
- Developers implement →
- System continuously validates that code matches design →
- Docs auto-update if divergence is detected.
Q: What is the status of your intellectual property and adoption?
A: We hold patent for the core AI engine, with Rinat Khabibullin and me listed as lead authors. The platform is already in active technical collaboration with NEC Corporation to modernize legacy COBOL systems used in critical banking infrastructure—where accurate, real-time documentation is a regulatory necessity.
In parallel, we are in advanced discussions with engineering teams at multiple Fortune 500 companies in the U.S. and Europe to integrate CRUDERRA into their internal developer platforms. These organizations face the same challenge: documentation that lags behind code creates security, compliance, and scalability risks—and our solution closes that gap automatically.
This real-world adoption by global technology leaders confirms that CRUDERRA is not just a prototype, but a production-grade platform solving a universal engineering problem.
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