AI idea: internal legislative radar (Czech Republic + EU) with alerts to Teams and Slack

Companies today run into the same problem: changes in laws, decrees, methodologies, and European regulations come from multiple sources, at different speeds, and with different structures. If the legal, compliance, or product team does not have a single place where changes are continuously captured, sorted, and distributed to the responsible people, delays arise. This quickly turns into operational risk: delayed updates to internal policies, poorly configured contract templates, outdated product documentation, or an unnecessarily late response to European regulation.

This is exactly where an internal legislative radar makes sense. Not as another passive database, but as an active AI project that monitors selected sources for the Czech Republic and the EU, identifies relevant changes, summarizes their content in an understandable form, and sends alerts to the tools the company already uses every day: Microsoft Teams and Slack. If you are also dealing with a broader selection of suitable tools, it is worth reviewing the overview at AIVýběr or thematic articles dedicated to AI tools.

In this guide, we will build the project practically and without jumps in difficulty. The goal is not a theoretical overview, but a functional first version that can be deployed in internal operations within a few weeks.

Introduction

An internal legislative radar is a combination of four layers: collecting data from reliable legal sources, rules or AI-based relevance classification, generating a concise summary, and distributing alerts to responsible teams. In the Czech environment, you typically monitor at least the Collection of Laws and International Treaties, ministry websites, the Office for Personal Data Protection, the Czech National Bank depending on the sector, and possibly the Public Administration Portal. At the EU level, EUR-Lex, the European Commission, the EDPB, or sector-specific agencies come into play.

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The key is that the system must not send everything out. A good radar does not work as raw monitoring, but as a filter with clear rules: who the change affects, what impact it has, by when action is needed, and who should be notified. AI does not take over legal responsibility here. It speeds up triage, summarization, and topic assignment, while the final interpretation is confirmed by a human.

Project goal

The goal of the project is to create an internal service that:

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checks defined legislative and regulatory sources for the Czech Republic and the EU daily,
detects new or changed documents,
sorts them by impact area, for example HR, business, IT, security, finance, or compliance,
generates a short management summary and a longer expert summary,
sends alerts to Microsoft Teams and Slack only to relevant groups,
stores an audit trail: source, processing time, prompt used, model version, and human approval.

The practical result: instead of manually going through dozens of websites, the company receives a standardized alert within hours of a change being published. The legal team spends less time on triage and more time on actual impact interpretation.

Prerequisites

Before you begin, verify four basic prerequisites.

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You have a designated process owner. Ideally the head of compliance, an internal lawyer, or a governance manager. Without an owner, the system quickly turns into a technical experiment without operational responsibility.
You have a list of priority sources. For example, the Collection of Laws, EUR-Lex, the Office for Personal Data Protection, the Czech National Bank, and two to three sector-specific institutions depending on the industry.
You have communication channels. Microsoft Teams and Slack must have approved incoming webhooks, or alternatively a Power Automate app or Slack app.
You have decided on the AI platform. A practical choice is often Azure OpenAI Service if the company uses the Microsoft ecosystem and addresses enterprise security, or the OpenAI API combined with its own backend where there is room for faster development. Official information can be found at Azure OpenAI Service, Microsoft Power Automate, Slack API and EUR-Lex.

If your company is only now looking for realistic deployment scenarios, useful context is also offered by articles on AIVýběr in the AI automation section, where similar types of internal workflows can be compared.

Implementation steps

Step 1: Define the legislative scope and priorities

What and why: First, determine which legal sources and topics the radar should monitor. Without this phase, you will collect too many documents and overwhelm users. The first version should be narrow and precise, not broad and noisy.

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How exactly: With the legal and compliance team, create a table with the columns: source, URL, document type, topic area, priority, check frequency, target channel in Teams or Slack, review owner. Start with 8 to 15 sources. For the EU, distinguish between regulations, directives, opinions, methodologies, and consultations, because they differ in impact and timing.

Specific input: List of sources: Collection of Laws, EUR-Lex, Office for Personal Data Protection, Ministry of Finance, Czech National Bank.

Specific output: Approved monitoring matrix with priorities P1 to P3 and assigned channels #compliance-alerts and #product-regulation.

Success metric: At least 90% of relevant sources for priority departments are covered in the first version without exceeding 15 sources in total.

Once you know what you are monitoring, you can follow up with technical data collection. This avoids a situation where the backend is built before the rules it is supposed to fulfill.

Step 2: Ensure data collection from official sources

What and why: The radar needs a stable inflow of data. Sources come in different forms: RSS, HTML pages, PDF documents, or public APIs. The goal is to obtain new items reliably, with the download time and a link to the original.

How exactly: For each source, choose the simplest official route. If RSS or an API exists, use it preferentially. If not, deploy an HTML scraper with clearly defined selectors. Store downloaded documents in their original form in object storage, for example Azure Blob Storage or Amazon S3, and metadata in a database, for example PostgreSQL. Each record must contain source_id, title, url, published_at, fetched_at, content hash, and processing status.

Specific input: RSS feed or page with new regulations on EUR-Lex and the document detail URL.

Specific output: Database table documents_raw with new items and stored original PDF or HTML files.

Success metric: Successful download for at least 98% of planned source checks over 14 days and less than 1% duplicates after hash-based deduplication.

Once the data is flowing in, it needs to be cleaned and converted into a form that both the downstream logic and the language model can work with effectively.

Step 3: Normalize content and remove noise

What and why: Official documents contain website navigation, footnotes, duplicates, tables, or scans. Without normalization, both relevance classification and AI summaries will be less accurate. The goal is to prepare clean text and standard metadata.

How exactly: Convert HTML to text using a parser, for example Beautiful Soup or Readability. Process PDFs in two ways: extract text-based PDFs with a library such as pdfplumber, and send scanned documents to OCR, for example Azure AI Document Intelligence or cloud OCR services. Then add language, jurisdiction, document type, institution, and regulation number if recognizable. Finally, split longer texts into logical blocks by paragraphs or chapters.

Specific input: A PDF decree from a ministry website and an HTML page with a press release from a regulatory authority.

Specific output: Table documents_normalized with clean text, metadata, and blocks prepared for classification and summarization.

Success metric: For at least 95% of documents, extracted text is produced with OCR error rates below the internally defined threshold, for example less than 3% unreadable tokens.

Only now does it make sense to decide what is truly important for the company. Without quality text, relevance would be assessed only superficially.

Step 4: Classify relevance and assign impact

What and why: Not every change deserves an alert. You need to distinguish whether a document affects your business, which departments it concerns, and how urgent it is. This layer combines a rule-based filter with AI classification.

How exactly: First create simple rules: keywords, institution names, legal areas, references to specific types of company activities. On top of that, run AI classification with a fixed taxonomy, for example: area = personal data protection, labor law, AML, cybersecurity, consumer, finance; impact = low, medium, high; action = monitor only, legal analysis, process change, documentation change. Always provide the model only with cleaned text and the internal description of the company or department, not unnecessary sensitive data. Store the output in a structured way as JSON, not just as free text.

Specific input: Normalized text of a draft European regulation and the company’s internal profile: e-commerce, 250 employees, operating in the Czech Republic and Slovakia.

Specific output: JSON with fields relevance=true, domains=["consumer","privacy"], impact="medium", owner_team="legal".

Success metric: Relevance accuracy of at least 85% against a manually labeled validation sample of 200 documents and alert precision of at least 80%.

Once the system knows that a document is important, it must turn it into a message that is understandable and quickly usable in operations.

Step 5: Generate summaries, recommended action, and deadlines

What and why: An alert without context only leads to more clicking. The point of AI is to provide a short, precise summary: what changed, who it affects, what the likely impact is, and what the team should do next. This layer saves time for both lawyers and managers.

How exactly: Prepare two outputs. The first is a management summary of up to 600 characters for Teams and Slack. The second is an internal expert note of 5 to 8 points with citations of key passages and a link to the original. In the prompt, strictly require that the model state only claims supported by the document text, distinguish between an effective regulation and a draft, and always list the effective date if available. Also add a “human verification required” field if the model is uncertain about the classification or effective date.

Specific input: A document marked as relevant with metadata about the type of regulation and impact area.

Specific output: A short summary for the Teams channel, a longer internal summary, and a list of proposed actions such as “review consent templates” or “update retention periods.”

Success metric: The legal team marks at least 80% of summaries as “usable without substantial edits” in a sample of the first 100 alerts.

Once you have a quality summary, all that remains is to deliver it to people so they do not overlook it while also not being overwhelmed by unnecessary noise.

Step 6: Connect notifications to Microsoft Teams and Slack

What and why: The value of the radar becomes apparent only when the information reaches the right people at the right time. Teams and Slack are ideal because everyday internal communication already takes place there.

How exactly: In Microsoft Teams, use incoming webhooks or a Power Automate flow that accepts JSON and sends an adaptive card with a title, summary, impact level, effective date, and a button to the detail. In Slack, use Incoming Webhooks or an app via the Slack API and send a structured message with Block Kit blocks. Set routing rules: high impact goes to the central channel and the specific team, medium impact only to the responsible team, low impact to the weekly digest. Duplicate suppression is also important: if a document is updated multiple times during the day, combine notifications into a single thread.

Specific input: Notification JSON with fields title, summary, impact, effective_date, source_url, owner_team.

Specific output: A message in the Teams channel “Compliance Alerts” and a parallel message in the Slack channel “#legal-updates.”

Success metric: Delivery of at least 99% of notifications without technical error and an open or response rate for high-impact items of at least 70% within 24 hours.

Distribution is done, but without an internal interface and records, the project becomes a black box. That is why you should add visibility and auditability.

Step 7: Create an internal overview, audit trail, and escalations

What and why: You need to know what was captured, to whom it was sent, who took ownership of the change, and whether the AI output was confirmed or corrected. This is important both for operations and for internal audit.

How exactly: Build a simple internal dashboard on top of the database in Power BI, Metabase, or an internal web application. Each record should show the source, original document, AI classification, summary, human verification status, notification history, and final decision. Add escalation rules: if no one confirms a high-impact item within 24 hours, an alert is sent to the head of the legal or compliance team.

Specific input: History of processed documents for the last 30 days and notification logs.

Specific output: A dashboard with filters by jurisdiction, impact area, verification status, and response SLA.

Success metric: 100% of high-impact alerts have a traceable audit trail and at least 95% of them are taken over by the responsible person within the defined SLA.

This makes the first version technically complete. Before you release it into normal operation, it is necessary to verify quality on real data and fine-tune the thresholds.

Testing

Divide testing into three layers.

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1. Technical tests: verify source availability, parsing, OCR, deduplication, processing queues, and delivery to Teams and Slack. Also simulate a source outage or a change in HTML structure.

2. AI quality tests: prepare a validation set of at least 200 documents from the last 6 to 12 months. Label them manually for relevance, impact area, priority, and recommended action. Then compare the AI output with the human labels. Track precision, recall, and the number of false negatives, because these are the riskiest cases for a legislative radar.

3. User tests: select 5 to 10 people from the legal, compliance, HR, and product teams. For two weeks, monitor whether the alerts are understandable, whether they arrive in the correct channel, and whether they produce noise. Ideally, use an evaluation form with a scale from 1 to 5 for relevance, clarity, and actionability.

A reasonable starting point is a state where the system captures most relevant changes, the number of unnecessary alerts is low, and the legal team does not have to substantially rewrite the summaries. If relevance fails, go one step back and adjust the taxonomy or filter rules before tuning the prompts themselves.

Deployment

Do not deploy everything to production at once. A three-phase approach is safer.

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Pilot: 2 to 4 weeks, a limited number of sources and one or two channels. Notifications go only to selected people and every output is subject to human verification.
Controlled operation: expansion to more sources, activation of automatic digests, and limited automatic distribution for low and medium impact.
Full operation: fixed SLAs, dashboard, escalations, regular monthly quality reporting, and quarterly source reviews.

From an operational perspective, pay particular attention to these points: managing credentials through a secure vault such as Azure Key Vault, logging prompts and responses with a retention policy, versioning classification rules, and clearly stating that the AI summary is not a legal opinion. If the company uses Microsoft 365, it makes sense to build the entire flow within Azure and Power Platform because of identity management and traceability.

Limits

A legislative radar is not a replacement for a lawyer, and it is good to acknowledge that right at the project design stage.

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AI may misinterpret context. Especially in amendment points, transitional provisions, and documents that refer to another regulation.
Not all sources have a stable structure. Authority websites change HTML and the scraper may break without warning.
Relevance depends on the industry. What is crucial for a bank may be irrelevant for a manufacturing company.
The effective date and practical impact may not be clear from a single document. It is often necessary to trace related materials or the explanatory memorandum.
Notification fatigue is a real risk. Too many alerts will devalue even a good system.

A practical rule is: automate collection, sorting, and the first summary, but leave the final conclusion about the company’s obligations to a human. The project has the highest value when it shortens the time from publication of a change to the first internal assessment.

FAQ

Does a language model have to be part of the project right from the start?

No. The first version can also be built in a rule-based way: collection, deduplication, simple keyword matching, distribution. But AI significantly improves summaries and relevance sorting, so it makes sense no later than in the second phase.

Is Teams or Slack better?

It depends on what the company uses as its main communication environment. In a Microsoft 365 environment, deployment via Teams and Power Automate is usually simpler. Slack is very strong where internal automation already runs on top of the Slack API.

How often should the radar check sources?

For priority sources, usually every hour to every four hours during working hours. For less important sources, a daily check is enough. More important than extreme frequency is stability and a low number of outages.

How can hallucinations in summaries be reduced?

Use a strict prompt, require citations or references to specific passages, separate facts from impact estimates, and introduce human verification for high-priority or uncertain outputs.

How many people are needed for implementation?

For the first version, a small team is usually enough: one owner from the legal or compliance department, one backend or integration developer, and one person for data or AI logic. In a larger company, a platform and security administrator is added.

What KPIs should be tracked after deployment?

Source coverage, relevance precision and recall, number of false negatives, time from publication to processing, alert acknowledgment rate, share of summaries approved without edits, and number of duplicate notifications.

Conclusion

An internal legislative radar for the Czech Republic and the EU is one of the most practical AI projects for companies that need to respond quickly to regulatory changes but do not want to expand manual monitoring. The greatest benefit does not come from the “AI summary” itself, but from a well-designed process: selecting the right sources, reliable collection, accurate relevance classification, a clear audit trail, and delivery to Teams and Slack without unnecessary noise.

If you proceed step by step, start with a narrow pilot, and set up human oversight for important cases, you can gain a usable internal system within a few weeks. The result is measurable: shorter response time, less manual reading of sources, and a better overview of which change requires real action and which only informational monitoring.

Recommended AI stack for implementation

Choose tools according to your budget and level of automation. Below is a direct overview of services for implementing the project.

Service	Service description	Offer
NordVPN	VPN service for privacy protection and secure connections.	Open offer
Semrush	SEO and marketing platform for analysis and traffic growth.	Open offer
Make	Advanced visual automation for workflows and integrations.	Open offer
Hostinger	Web hosting and domains for fast website launch.	Open offer
Fiverr	Marketplace for freelancers and external specialists.	Open offer
Adobe	Creative tools for graphics, video, and digital content.	Open offer
Canva	Online design tool for graphics, presentations, and social media.	Open offer
Jasper	AI tool for marketing copy and content campaigns.	Open offer