com.praxikon/praxikon

Tools

• answer_question

  Use this for a short, general question about the EU AI Act or the GDPR (AVG), such as whether a customer service chatbot has to say that it is AI or when a DPIA is required. It looks the question up in the published Praxikon answers: deterministic, no language model, and with the articles and sources. Before you call it, rewrite the question in general terms in at most 200 characters: no names of people or organisations, no contact details, no numbers that identify a person or a case. A question that looks like it contains personal data is refused. The question is not stored, logged or counted; only the use of the tool is counted per day. An unrecognised question returns no answer rather than a constructed one, and so does a question about law outside the EU: Praxikon covers only the EU AI Act and the GDPR. For one concrete system or case, the coded assessment tools give a fuller answer.

  shallow
• check_case

  Use this when the user describes a concrete, intended use of AI or of personal data (a system, a project, a supplier) and wants to know whether a DPIA, a FRIA or duties under the GDPR (AVG) and the EU AI Act come into play. It runs the Praxikon case check: fixed rules over fixed answers, the same rules as the case check on the site. It returns per law (GDPR, AI Act, and where the two meet) each test with its outcome, the article and a source link, the DPIA and FRIA signal, the next steps and the facts that are still open. Fill in the answers yourself from what the user said, and ask the user for a fact that decides a test rather than guessing it. Leave out what is not known: it counts as unknown, never as no. Send only these coded answers and never the case text, names or other personal data; there is no field for them. Show the notice with the result. It does not build the implementation graph or the obligation list of one AI system.

  shallow
• classify_system

  Use this as the first step when you hold a concrete AI system and need to know which routes of the EU AI Act come into view: prohibited practice screening, Annex III high risk, transparency, FRIA and the general purpose model route, and with a version 2 profile the wider set of duties, such as Article 26 for a deployer. It runs the deterministic decision engine over coded answers and returns one applicability status per assessed obligation, with the timing and what is still open. It is not meant for a general question about the law. An obligation the engine did not assess is listed in assessment_scope.not_assessed with the reason; it was not found inapplicable. It does not return a single coded classification label.

  shallow
• explain_applicability

  Use this whenever you have to justify why an obligation does or does not bear on a case, and an outcome on its own is not enough. It returns the legal path: the conditions under which the rule is about a situation, the exceptions under which it is not despite those conditions, and the statements of the obligation split by kind, each with the primary source, the locator inside that source and the source record behind it. Supply a profile as well and the deterministic rule trace is added: which rule was met, not met or unknown for this case, plus the assumptions and the questions the engine could not settle. Use this rather than paraphrasing an obligation yourself: the conditions, the exceptions and the citations are published data and are not to be reconstructed from prose. Take the identifier from earlier tool output; a short form such as art. 26 resolves when it points at one obligation, else error_code and suggestions come back. A question without an identifier belongs with the short question tool or the case check.

  shallow
• export_dpia_intake

  Use this when the user wants the DPIA intake of a concrete, intended use of AI or of personal data as a Word file to work on, and, where the FRIA comes into play, the FRIA draft too. It takes the same fixed fact answers as the case check of Praxikon, as coded choices, and gives the same documents as the download on the site: the four parts of Article 35(7) GDPR with the seventeen points of the Dutch government model DPIA, each point filled from the facts given, partly filled, or left as an open question, with articles and source links. Leave out what is not known: it counts as unknown and becomes an open question, never a no. Send only the coded answers and never the case text, names or other personal data; there is no field for them. The answer gives a download_url: the case check on the site with the answers in the part after #, which the browser never sends to the server; opening it shows the outcome and builds the Word file in the user's browser. Give the user that link, the explanation and the summary, and show the notice. Nothing is stored.

  shallow
• get_applicable_obligations

  Use this when you need the obligations themselves for one assessed system rather than the classification picture: which obligations apply, which possibly apply, from when, why, and on which source and locator. Returns one entry per assessed obligation, filtered by applicability. A version 2 profile has the wider set assessed, including Article 26 for a deployer of a high risk system, with one sub result per paragraph. This is the assessed route, not a catalogue of all obligations in the dataset.

  shallow
• get_national_enforcement

  Use this for the national layer: which authority is designated in a member state, how far the implementing law has come, and which dated enforcement signals are recorded, each with a primary source. The authority is recorded as free text per member state and is deliberately not a graph identifier, because which national body fills the market surveillance role differs per member state. The same call returns the ruling register: judgments and supervisory decisions with the relation they bear to a provision (interprets, narrows, broadens, confirms, contradicts, or illustrates for context). An event says what happened, a ruling says what we know a provision means. Never present a related_gdpr ruling as AI Act case law: it rests on the GDPR and carries the illustrates relation only. This tool takes no lang: the tracker carries both languages on every record, in the fields that end in _nl and _en.

  shallow
• get_source_provenance

  Use this before you quote, cite or hand on anything from this graph, and whenever a reader asks on what authority a statement rests. It returns, for one object, every statement with its kind, its primary source, the locator inside that source and the review metadata, plus the source records behind those citations and a ready made citation block with the identifier, the object version, the payload hash and the release. Works for any object type, not only obligations. Take the identifier from earlier tool output; a short form such as art. 26 resolves when it points at one obligation, else error_code and suggestions come back.

  shallow
• request_expert_review

  Use this only when the user asks for it in their own words: they want to talk to a person, ask for a human expert, or ask how to get in touch about their situation. Never call it on your own initiative, never as a closing suggestion, and never because an answer seems uncertain. It returns a link to book a 30 minute introductory call with Zahed Ashkara, an independent AI and privacy consultant behind Praxikon. Send no part of the conversation: the tool takes only a language and, optionally, a topic from a fixed list, and nothing about the user. Show the title, the sentence and the link to the user as they are, and let the user decide whether to book.

  shallow

Embed this server’s score

Tool count and median score across every tool in this server’s corpus — honest in a way a single cherry-picked tool’s badge wouldn’t be.

[![Vouch score](https://vouch.tools/api/servers/1a46fa3c-0df9-487c-bf79-b8f9bcfc4325/badge.svg)](https://vouch.tools/servers/1a46fa3c-0df9-487c-bf79-b8f9bcfc4325)