What is Ahmed Mohammed's expertise in AI/ML?

Ahmed Mohammed is an AI/ML Engineer specializing in computer vision, diffusion models, and out-of-distribution detection. He achieved 99.03% AUROC on CIFAR-10 OOD detection in his Master's thesis at JKU Linz and 98.4% defect detection accuracy at PROFACTOR GmbH. He is proficient in PyTorch, YOLOv8, conditional diffusion models, and full-stack ML engineering.

Faultrix is an AI-powered construction quality control SaaS founded by Ahmed Mohammed. It analyzes building photos and generates ÖNORM B 2110-compliant technical reports in under 1 minute. The platform features SHA-256 evidence chain, DSGVO compliance, and AES-256 encryption. Visit faultrix.com for more information.

Where is Ahmed Mohammed based and how can I contact him?

Ahmed Mohammed is based in Linz, Austria. He can be contacted via email at ahmed.mo.0595@gmail.com, on LinkedIn at linkedin.com/in/ahmed-3m, or through his portfolio at ahmed-3m.github.io. He is open to senior AI/ML roles and research collaborations.

What industries does Ahmed Mohammed work with?

Ahmed Mohammed works primarily with industrial manufacturing, construction, and quality control sectors. His work at PROFACTOR GmbH focused on industrial anomaly detection for inkjet-printed building components (Zer0P project, funded by Upper Austria government). Faultrix serves the Austrian construction industry with AI-powered building analysis and ÖNORM-compliant reporting.

What is Ahmed Mohammed's educational background?

Ahmed Mohammed holds a Master of Science in Artificial Intelligence from Johannes Kepler University Linz (JKU), where his thesis on conditional diffusion models for out-of-distribution detection was supervised by Prof. Sepp Hochreiter (inventor of LSTM). He also holds a Bachelor of Science in Mechatronics Engineering from Eastern Mediterranean University in Cyprus.

What was Ahmed Mohammed's Master's thesis about?

Ahmed Mohammed's Master's thesis at JKU Linz (2026) is titled 'Conditional Diffusion Models as Generative Classifiers for Out-of-Distribution Detection'. He achieved 99.03% AUROC on CIFAR-10, improving the baseline by 18.8 percentage points through a novel class-conditional separation loss. The work was supervised by Prof. Sepp Hochreiter and applied to industrial quality control with multi-head conditioning.

Is Ahmed Mohammed available for hire or freelance AI/ML projects?

Ahmed Mohammed is open to senior AI/ML engineering roles and research collaborations. He specializes in computer vision, diffusion models, and out-of-distribution detection. Based in Linz, Austria, he is available for positions in the DACH region and remote roles. Contact: ahmed.mo.0595@gmail.com or LinkedIn: linkedin.com/in/ahmed-3m

What AI services does Faultrix offer for the construction industry?

Faultrix is an AI-powered construction quality control SaaS. It analyzes building site photos and generates ÖNORM B 2110-compliant technical reports in under 1 minute. The platform provides SHA-256 evidence chains for legal defensibility, DSGVO-compliant data handling, and AES-256 encryption. It serves building inspectors, construction companies, and real estate developers primarily in Austria, Germany, and Switzerland. Visit faultrix.com for details.

What types of AI problems does Ahmed Mohammed solve?

Ahmed Mohammed specializes in: (1) industrial defect detection and anomaly detection systems using YOLO and diffusion models — achieving 98.4% accuracy in production at PROFACTOR GmbH; (2) out-of-distribution detection for production AI safety — 99.03% AUROC on CIFAR-10 benchmark; (3) full-stack AI product development, from research to deployed SaaS (Faultrix); (4) computer vision pipelines for manufacturing and construction industries.

What is Faultrix and what does it do?

Faultrix is an AI-powered construction quality control SaaS. Building inspectors upload photos of a construction site, and Faultrix analyzes them and generates a legally compliant ÖNORM B 2110 technical report in under 1 minute with SHA-256 evidence chain and DSGVO compliance.

What tech stack does Faultrix use?

Next.js 14 App Router with TypeScript, Convex for database and backend logic, Clerk for authentication, Cloudflare R2 for file storage, OpenAI API (GPT-4o + Vision) for AI analysis, and Stripe for payments.

Shipping Faultrix: What I Learned Building an AI SaaS From Zero in 5 Months

## What I Built

[Faultrix](https://faultrix.com) is an AI-powered construction quality control SaaS. A building inspector uploads photos of a construction site; Faultrix analyzes them and generates a legally compliant ÖNORM B 2110 technical report — with SHA-256 evidence chain, DSGVO compliance, AES-256 encryption — in under 1 minute.

I built it solo, from zero to production, in 5 months. Coming from a pure research background (diffusion models, OOD detection at JKU Linz), here's what surprised me about the gap between AI research and AI product.

The Tech Stack and Why

Frontend & Backend: Next.js 14 App Router with TypeScript. React Server Components make the AI streaming UX straightforward. TypeScript caught enough bugs early that the extra setup was worth it on day 1.

Database & Backend logic: Convex. Real-time reactive queries, serverless functions, and built-in file storage. For a solo developer building a data-intensive SaaS, Convex's all-in-one approach beats stitching together Supabase + separate serverless functions.

Authentication: Clerk. Complete auth in 2 hours including organization management and webhooks to Convex. The opportunity cost of building auth yourself in 2025 approaches infinity.

File storage: Cloudflare R2. S3-compatible API, zero egress fees, and edge distribution. For image-heavy SaaS this is a meaningful cost difference at scale.

AI layer: OpenAI API (GPT-4o + Vision). The construction defect analysis uses vision + structured output extraction to produce JSON that maps to the ÖNORM report schema.

Payments: Stripe. 45 minutes to working checkout, webhooks handled, subscription management done.

Security: SHA-256 evidence hashing on every uploaded image (makes the report legally defensible), AES-256 encryption at rest for all documents.

The Hardest Part Wasn't the AI

I expected the AI part to be hard. It wasn't. The GPT-4o vision API is remarkably capable at identifying construction defects when given a well-structured prompt and output schema.

The hard parts: - ÖNORM compliance: The Austrian standard for construction technical documentation is not AI-friendly. Getting the output to match the exact required section structure, terminology, and formatting took 3 weeks of iteration with real building inspectors. - Evidence chain: A report is legally defensible only if you can prove the photos weren't altered. The SHA-256 pipeline sounds simple but the UX of surfacing it without confusing users is non-trivial. - Pricing: "How much should AI-generated ÖNORM reports cost?" has no obvious answer. I interviewed 12 building inspectors and construction companies before landing on a pricing structure.

What Research Background Actually Transfers

Transfers well: - Systematic experimentation (prompt engineering is just hyperparameter tuning with words) - Evaluation rigor — I built proper test suites for the AI outputs before shipping - Understanding model limitations — knowing when to trust GPT-4o and when to flag for human review

Does not transfer: - Product intuition — researchers optimize for correctness; users optimize for "does this save me time" - Distribution shift in production is a UX problem, not just a model problem - You cannot ablate your way to product-market fit

One Thing I'd Tell a Research-to-Product Engineer

The first version of Faultrix had a beautiful, fully accurate AI analysis pipeline and a terrible UX. Users churned because the interface required 8 steps to upload photos. The AI was irrelevant — the UX was the product.

Ship the simplest version that delivers the core value (in Faultrix's case: "upload photos → get report"). Then add sophistication. Do not add sophistication before you have users.

Try it at [faultrix.com](https://faultrix.com). If you're in construction or building inspection in the DACH region, I'd love your feedback — ahmed.mo.0595@gmail.com.