Victor Purcallas Marchesi
Victor Purcallas Marchesi is an individual software engineer and creator of quodeq, an open-source AI code-quality and security auditor. The project is intended to close the gap between traditional static analysis and the kinds of structural, intent-level issues a senior reviewer can find, while keeping evaluation transparent and local-first.
At a Glance
- Individual software developers
- Small and medium software teams
- Open-source projects
- Engineering teams needing repository-wide security and quality review
- +2 more
AI Tools by Victor Purcallas Marchesi
(1)
Quodeq
Open Source AI Code Quality Scanner
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Latest News
Now it is our turn — essay on generative AI's impact on skilled work and the social bargain around software
A first calibration — publication of quodeq's first AI code-review calibration against 2,740 security tests and five models
God Does Not Play Dice. We Built Software That Does. — essay on probabilistic AI software and what scale changes
Europe's Fable: The Off Switch Was Never Ours — essay on AI sovereignty and local alternatives
Products & Services
Open-source AI-powered code-quality and security scanner/auditor. It evaluates a repository with a user-selected AI model, reports findings with file, line, reason, code evidence and CWE references, scores six ISO 25010 dimensions plus custom standards, and provides dashboards, trends, code maps, violation triage and fix plans.
Desktop distributions attached to releases for macOS and Windows; the normal installation paths remain pipx and Homebrew on macOS.
Market Position
quodeq positions itself between rule-based static-analysis tools and human-style code review: it uses language models to reason about structure, intent and context, but exposes its rules, evidence and source code. Its public OWASP benchmark compares it with CodeQL, Semgrep OSS, FindSecBugs, SonarQube and ZAP; the differentiators claimed by the creator are MIT transparency, local execution, user-selected models, no account/telemetry/server, and no paid enterprise gate.
Leadership
Founders
Victor Purcallas Marchesi
Independent software engineer with a political-science background. His public GitHub profile says he works as a software engineer, builds software in Amsterdam, and is associated with DPGMedia; he is the author and copyright holder of quodeq's MIT-licensed source and the sole named author on the project's official essays and benchmark page.
Executive Team
Victor Purcallas Marchesi
Creator / maintainer
Software engineer with a political-science background; he maintains the quodeq repositories, writes the official product essays and benchmarks, and publishes the software under his own name.
Founding Story
Victor started quodeq to address the gap between what linters and rule-based static-analysis tools catch and what an experienced engineer can identify in a code review: logic flaws, vulnerabilities, architectural weaknesses, and future maintenance problems. His stated initial vision was an auditable, MIT-licensed code-quality compass that uses language models to reason across an entire codebase, runs on the user's machine, has no account or telemetry, and does not require a proprietary enterprise contract.
Business Model
Revenue Model
The project is distributed as free MIT-licensed software with no enterprise tier, paid tier, account or quodeq server. Users bring their own AI provider and its billing; local providers can run entirely on the user's machine. The public sources do not describe subscription, API or licensing revenue for Victor's project.
Pricing Tiers
The official introduction says there is no freemium gate, no enterprise tier and no contact-sales model; the terms and repository identify the MIT license. Users may incur separate charges from the cloud model provider they choose.
Target Markets
- Individual software developers
- Small and medium software teams
- Open-source projects
- Engineering teams needing repository-wide security and quality review
- Organizations with custom standards or CI/SARIF workflows
- Users requiring local/offline code analysis
- Whole-repository code-quality and security review
- Finding vulnerabilities and design flaws that conventional linters or static analysis may miss
- Pull-request review and continuous integration checks
- Tracking quality and technical debt across repeated evaluation runs
- Teams applying their own coding, architecture, compliance or industry standards
- Privacy-sensitive or sovereignty-sensitive analysis using Ollama, llama.cpp or omlx locally