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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchOpencomplai says its compliance pass/fail path uses deterministic rules, not LLM or machine-learning inference—and that its continuous integration (CI) checks are designed to keep unapproved AI packages out of that path. The project describes this as a design constraint, while allowing an optional AI plugin for a narrower task: classifying free-text system descriptions.
What Opencomplai keeps out of its compliance decision path
According to Opencomplai, its core rule engine in packages/core and its command-line interface make compliance decisions without LLM or ML inference. The project’s engineering article, published on 24 September 2026, puts the point plainly: “That’s a design constraint, not a line for the landing page, and I don’t just assert it.” The statement is attributed to the Opencomplai Team, not to an individual author. Read the engineering article.
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This is Opencomplai’s stated architectural choice, not a general rule for compliance software. It also does not establish that deterministic rules are legally sufficient or that using an LLM in every compliance workflow is unsafe. The publisher’s blog index lists the article in its engineering notes, but does not independently verify its technical claims. See the blog index.
How CI is intended to enforce the dependency boundary
Opencomplai says that on each pull request, CI scans pyproject.toml, package.json, and requirements*.txt files across the repository for unapproved AI or LLM packages. The article says tests, examples, and fixtures are excluded so demos and test data can use dependencies without bringing them into the decision-making code.
For example, the article says a new transformers import in the risk engine should make the build fail if it has not been approved. In practical terms, this is a dependency-policy check: it aims to catch a prohibited package entering the relevant codebase through a pull request, rather than relying only on developers to remember the architectural rule.
Where the optional AI plugin fits
Opencomplai describes an opt-in plugin in packages/ai that includes an ONNX/transformers intent classifier. It says a separate optional [deep] extra supports local GGUF models through llama-cpp-python. The plugin is described as sandboxed to its own package and separate from the compliance pass/fail decision.
The plugin’s stated role is to classify free-text descriptions of systems. Opencomplai specifically says it does not decide whether Article 5 applies. That is the publisher’s description of the boundary; the article does not provide an independent technical audit of the implementation.
What a developer can take from the design
- Separate decision-making from assistance. Opencomplai’s stated architecture reserves compliance outcomes for its deterministic rule engine while allowing an optional classifier for descriptive text.
- Enforce policy in the change workflow. The described pull-request scan is meant to flag unapproved AI dependencies before they enter the decision path.
- Make exceptions explicit. Excluding tests, examples, and fixtures allows those areas to use packages without silently changing the stated rule for production decision code.
These are useful design ideas, but the article alone does not show the scan configuration, its handling of transitive dependencies, or whether every possible route for introducing inference is covered. It should be read as a first-party account of Opencomplai’s approach, not as proof of a completed security or compliance audit.
Installation and project links
The article gives this installation command:
pip install opencomplaiFor project documentation and source code, use the links provided by Opencomplai in its engineering article.
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