AI Model Governance MCP Server

AI model governance intelligence for AI agents, compliance teams, and legal counsel — delivered via the Model Context Protocol. This MCP server gives your AI agent live access to US federal regulations, congressional AI bills, EU AI Act signals, academic safety research, and open-source audit tooling through 8 purpose-built tools that score, classify, and explain the AI governance landscape.

Built for Chief AI Officers, responsible AI teams, and compliance engineers who need regulatory intelligence integrated directly into their AI workflows. Every tool returns structured JSON with scores, evidence signals, and actionable recommendations — no manual research, no dashboard subscriptions.

What data can you access?

Why use AI Model Governance MCP Server?

Tracking AI regulation by hand means monitoring the Federal Register daily, reading congressional committee reports, searching ArXiv for relevant papers, and manually comparing your AI systems against NIST and EU AI Act requirements. A single governance audit takes a compliance analyst 3-5 days. Regulations change faster than manual review cycles can keep up.

This MCP server automates the entire intelligence pipeline. Your AI agent calls a tool, 8 data sources are queried in parallel, scoring algorithms classify the results, and a structured governance assessment returns in under 90 seconds.

• Scheduling — run regulatory landscape scans daily or weekly to track shifts before they become compliance deadlines
• API access — integrate governance intelligence into compliance pipelines from Python, JavaScript, or any HTTP client
• Proxy rotation — underlying actors use Apify's built-in infrastructure for reliable, unblocked data collection
• Monitoring — get Slack or email alerts via Apify webhooks when regulatory velocity spikes
• Integrations — connect results to Zapier, Make, Google Sheets, or push directly into GRC platforms via webhooks

Features

• 8 MCP tools covering the full AI governance intelligence stack: regulatory landscape, legislation tracking, risk classification, bias research, compliance gap analysis, enforcement search, emerging risk detection, and full audit assessment
• 4 independent scoring models — Regulatory Velocity Index, Research-Regulation Gap Analysis, Framework Alignment Score, and OSS Tooling Maturity Index — each returning a 0-100 score with labeled tier
• Composite governance verdict — weighted formula combining all 4 models (framework alignment 30%, OSS tooling 25%, regulatory velocity 25%, research-reg gap inverted 20%) produces a single WELL_GOVERNED to UNGOVERNED verdict
• Parallel data collection — all actor calls dispatch simultaneously with 120-second timeouts and 512MB memory allocation per actor, completing in 30-90 seconds regardless of how many sources are queried
• EU AI Act keyword detection — 6 EU AI Act signal terms including "general-purpose ai", "foundation model", "gpai", and "high-risk ai" are matched across all regulatory sources
• NIST RMF alignment signals — 5 NIST AI Risk Management Framework keyword patterns detect framework adoption across federal documents and policy websites
• Bias and fairness coverage — 6 bias keyword patterns including "disparate impact", "algorithmic accountability", and "transparency" surface fairness-related regulatory content
• Cutting-edge topic gap detection — 8 frontier research terms (foundation model, LLM, diffusion, multimodal, agent, autonomous, RLHF, alignment) identify research areas outpacing current regulation
• Research-to-regulation ratio — computes the ratio of research volume (ArXiv + Semantic Scholar) to regulatory volume (Federal Register + Congress) to identify domains where science is ahead of policy
• Spend controls built in — every tool checks the eventChargeLimitReached flag before executing and returns a clean error message if the spending limit is hit
• Standby mode operation — runs as a persistent HTTP server on Apify's standby infrastructure; no cold start delays for recurring governance workflows

Use cases for AI model governance intelligence

Enterprise AI governance team regulatory monitoring

Governance teams at large technology companies need to track US federal and EU AI regulatory developments without hiring a team of policy analysts. The ai_regulatory_landscape tool returns a live Regulatory Velocity Index — score, tier (DORMANT through ACCELERATING), and evidence signals — so teams know whether to escalate compliance resourcing before new rules take effect.

Chief AI Officer framework alignment reporting

CAIOs preparing board-level AI risk disclosures need a defensible mapping of their AI systems against NIST AI RMF and EU AI Act requirements. The risk_tier_classification tool accepts a specific use case (e.g., "automated credit underwriting", "hiring resume screening") and returns a Framework Alignment Score with NIST coverage count, EU AI Act reference count, and alignment tier from NON_COMPLIANT to COMPREHENSIVE.

Legal and compliance enforcement tracking

In-house legal teams and outside counsel monitoring AI enforcement trends use enforcement_action_search to track FTC, EEOC, and state attorney general actions on algorithmic systems. The tool filters Federal Register content for enforcement and penalty language, quantifies enforcement activity, and returns signals like "12 AI enforcement actions — active regulatory scrutiny in this sector."

Responsible AI research team bias monitoring

Responsible AI researchers need to stay current with the academic literature on algorithmic fairness, bias measurement, and model transparency. The bias_research_monitor tool queries ArXiv and Semantic Scholar in parallel, computes a Research-Regulation Gap score, and flags when cutting-edge bias research (foundation models, RLHF, multimodal systems) is not yet addressed by current regulations.

AI audit team tooling assessment

AI audit teams planning their governance toolkit need to know which open-source tools exist, how mature they are, and which audit categories are well-served versus underserved. The audit_tooling_assessment tool searches GitHub for repositories tagged with categories including audit, fairness, bias, interpret, monitor, governance, and compliance, then scores OSS Tooling Maturity (NASCENT through PRODUCTION_READY) based on star counts, activity dates, and tool category diversity.

Board-level AI risk reporting

Risk committees and boards receiving AI governance updates need a single, defensible number summarizing governance maturity. The audit_tooling_assessment full composite assessment produces a WELL_GOVERNED to UNGOVERNED verdict with a 0-100 composite score and a list of specific recommendations — suitable for executive reporting without requiring the audience to interpret raw regulatory data.

How to use AI model governance intelligence

1. Connect the MCP server to your AI client — Add the server URL https://ai-model-governance-mcp.apify.actor/mcp to your Claude Desktop, Cursor, Windsurf, or Cline configuration. You will need your Apify API token in the Authorization header.
2. Call the tool matching your need — Ask your AI agent "What is the current regulatory velocity for AI hiring algorithms?" and it will call ai_regulatory_landscape with the relevant topic. No code required for conversational use.
3. Review scores and signals — Each tool returns a numeric score, a labeled tier, and a list of evidence signals explaining what drove the score. The signals are human-readable findings like "7 AI bills in Congress — intense legislative activity."
4. Export or schedule results — For ongoing monitoring, trigger the same tool on a weekly schedule via Apify and export results as JSON to your GRC platform, Google Sheets, or compliance documentation system.

MCP tools

Tool parameter details

Connection examples

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "ai-model-governance": {
      "url": "https://ai-model-governance-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Cursor / Windsurf / Cline:

Use the same URL https://ai-model-governance-mcp.apify.actor/mcp with your Apify token as a Bearer token in the Authorization header. The server follows the MCP Streamable HTTP transport specification.

Direct HTTP call:

curl -X POST "https://ai-model-governance-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "risk_tier_classification",
      "arguments": {
        "useCase": "automated resume screening",
        "industry": "hiring"
      }
    },
    "id": 1
  }'

Output example

Full response from audit_tooling_assessment for topic "AI hiring algorithms":

{
  "topic": "AI hiring algorithms",
  "compositeScore": 61,
  "verdict": "ADEQUATE",
  "regulatoryVelocity": {
    "score": 74,
    "legislationCount": 9,
    "regulationCount": 7,
    "velocityLevel": "RAPID",
    "signals": [
      "7 AI-related federal regulations — active regulatory landscape",
      "4 proposed AI rules — more regulation incoming",
      "9 AI bills in Congress — intense legislative activity",
      "2 AI bill(s) advancing — near-term compliance impact"
    ]
  },
  "researchRegGap": {
    "score": 62,
    "researchVolume": 38,
    "regulationVolume": 16,
    "gapLevel": "SIGNIFICANT_GAP",
    "signals": [
      "Research-to-regulation ratio 2.4:1 — science ahead of policy",
      "14 cutting-edge AI research areas — many unregulated",
      "22 research outputs in 2025+ — rapid innovation pace"
    ]
  },
  "frameworkAlignment": {
    "score": 55,
    "nistCoverage": 6,
    "euAiActCoverage": 5,
    "alignmentLevel": "SUBSTANTIAL",
    "signals": [
      "6 NIST AI RMF references — framework adoption underway",
      "5 EU AI Act references — cross-jurisdictional compliance",
      "4 bias/fairness references — responsible AI practices addressed"
    ]
  },
  "ossTooling": {
    "score": 72,
    "repoCount": 18,
    "maturityLevel": "MATURE",
    "signals": [
      "5 popular repos (100+ stars) — mature OSS ecosystem",
      "11 recently active repos — vibrant development community",
      "6 tool categories available — comprehensive governance toolset"
    ]
  },
  "allSignals": [
    "7 AI-related federal regulations — active regulatory landscape",
    "9 AI bills in Congress — intense legislative activity",
    "Research-to-regulation ratio 2.4:1 — science ahead of policy",
    "6 NIST AI RMF references — framework adoption underway",
    "5 EU AI Act references — cross-jurisdictional compliance",
    "5 popular repos (100+ stars) — mature OSS ecosystem"
  ],
  "recommendations": [
    "Research far ahead of regulation — prepare for rapid regulatory catch-up",
    "Regulatory velocity high — establish dedicated AI compliance function",
    "Mature OSS tooling available — adopt open-source AI governance tools"
  ]
}

Output fields

Individual tools (non-audit_tooling_assessment) return a subset of these fields relevant to their specific scoring model, plus the raw source data (up to 15-25 items from each relevant data source).

How much does it cost to use AI model governance intelligence?

All 8 MCP tools use pay-per-event pricing — you pay $0.045 per tool call. There are no subscription fees, no monthly minimums, and no cost for idle time between calls.

Apify's free plan includes $5 of monthly platform credits — enough for 111 tool calls before any payment is needed. Set a maximum spending limit per run to cap costs automatically.

Compare this to dedicated AI governance platforms like Credo AI, Fairly AI, or TruEra at $25,000-100,000 per year. For teams that need regulatory intelligence integrated into AI agent workflows rather than a standalone dashboard, this server provides comparable data access at a fraction of the cost.

Use this MCP server via the Apify API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("ryanclinton/ai-model-governance-mcp").call(run_input={})

print(f"Run ID: {run['id']}")
print("MCP endpoint: https://ai-model-governance-mcp.apify.actor/mcp")
print("Connect your MCP client to the endpoint above using your Apify token.")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_APIFY_TOKEN" });

const run = await client.actor("ryanclinton/ai-model-governance-mcp").call({});

console.log(`Run ID: ${run.id}`);
console.log("MCP endpoint: https://ai-model-governance-mcp.apify.actor/mcp");
console.log("Connect your MCP client to the endpoint with your Apify token.");

cURL — direct MCP tool call

# Call the risk_tier_classification tool directly
curl -X POST "https://ai-model-governance-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "audit_tooling_assessment",
      "arguments": {
        "topic": "large language model deployment",
        "organization": "financial services firm"
      }
    },
    "id": 1
  }'

# Call the legislation_tracker tool
curl -X POST "https://ai-model-governance-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "legislation_tracker",
      "arguments": {
        "topic": "facial recognition"
      }
    },
    "id": 2
  }'

How AI Model Governance MCP Server works

Parallel data collection across 8 sources

When a tool is called, the server dispatches parallel requests to up to 8 Apify actors using Promise.all(). Each actor call allocates 512MB of memory and has a 120-second timeout. The 8 data sources are: Federal Register Search (US regulatory documents), Congress Bill Search (legislative activity), Eurostat Statistics (EU digital economy metrics), ArXiv Paper Search (AI preprints), Semantic Scholar Search (peer-reviewed literature), GitHub Repo Search (OSS tooling), Website Change Monitor (policy site tracking), and Website Content to Markdown (full document extraction). Lighter tools query 2-4 sources; the full audit_tooling_assessment queries all 8 simultaneously.

Four scoring models applied to raw data

Raw results from each source flow into one or more of four scoring functions. The Regulatory Velocity Index scores Federal Register AI regulations (max 30 points: 4 points per regulation, 3 per proposed rule), congressional AI bills (max 25 points: 3 per bill, 5 per advanced bill), EU activity via Eurostat (max 20 points), and policy website changes (max 25 points). The Research-Regulation Gap computes the research-to-regulation ratio, detects frontier research topics (foundation model, LLM, diffusion, multimodal, agent, autonomous, RLHF, alignment), and scores recent research acceleration (papers dated 2025 or later). The Framework Alignment Score pattern-matches 6 EU AI Act keywords, 5 NIST RMF keywords, and 6 bias/fairness keywords across all regulatory and web content, with a maximum of 35 points per framework and 30 for bias coverage. The OSS Tooling Maturity Index scores GitHub repositories by star count (repos with 100+ stars = 8 points each, max 50 combined), recency (active since 2025 = 3 points each), and tool category diversity across 9 categories (audit, fairness, bias, explain, interpret, monitor, governance, compliance, risk — 5 points each, max 30).

Composite assessment and verdict generation

The audit_tooling_assessment tool combines all four model scores using a weighted formula: framework alignment at 30%, OSS tooling maturity at 25%, regulatory velocity at 25%, and the research-regulation gap inverted (100 minus the gap score) at 20%. The gap is inverted because a larger research-regulation gap is a negative governance signal. Composite scores map to five verdicts: WELL_GOVERNED (75+), ADEQUATE (55-74), GAPS_PRESENT (35-54), SIGNIFICANT_GAPS (15-34), and UNGOVERNED (below 15). Recommendations are generated deterministically based on individual score thresholds — for example, framework alignment below 40 triggers the NIST AI RMF implementation recommendation, and regulatory velocity at ACCELERATING triggers the dedicated compliance function recommendation.

Standby mode and HTTP transport

The server runs as a persistent Express application using the MCP Streamable HTTP transport from @modelcontextprotocol/sdk v1.12.1. Each POST to /mcp creates a new McpServer instance and StreamableHTTPServerTransport, connects them, handles the request, and closes the transport when the response stream closes. This stateless-per-request design allows multiple concurrent tool calls from different agents without session conflicts. In non-standby mode (direct actor runs), the server starts briefly, logs a health confirmation, and exits cleanly after 1 second.

Tips for best results

1. Use audit_tooling_assessment for comprehensive governance work. It queries all 8 sources and applies all 4 scoring models. The composite verdict and recommendations are the most useful output for executive reporting or compliance documentation.

2. Use targeted tools for specific questions. If you only need to know whether new AI bills are advancing, legislation_tracker is faster and cheaper than the full assessment. If you are preparing for an EU AI Act audit, risk_tier_classification with your specific use case gives the most relevant framework alignment signals.

3. Include the industry parameter in risk_tier_classification. Specifying "healthcare", "finance", or "hiring" sharpens the Federal Register query and increases the relevance of returned regulations to your specific compliance context.

4. Schedule ai_regulatory_landscape weekly. The regulatory environment changes. A weekly scheduled run that exports results to Google Sheets gives your governance team a live velocity tracker without any manual monitoring effort.

5. Combine bias_research_monitor output with enforcement_action_search. Research identifying bias risks often precedes enforcement action by 12-18 months. If bias_research_monitor returns a SIGNIFICANT_GAP or CRITICAL_GAP for your AI domain, treat it as an early warning for enforcement attention in that area.

6. Pass organization to compliance_gap_analysis. Describing your organization type (e.g., "federally chartered bank", "EU-based HR software vendor") improves the precision of the Federal Register and framework content queries.

7. Set a spending limit before running automated workflows. Use Apify's run spending limit feature to cap costs if you are scheduling tools to run frequently. At $0.045 per call, 100 calls costs $4.50 — well within the free tier, but good practice to set a hard ceiling for automated pipelines.

Combine with other Apify actors

Limitations

• US and EU jurisdiction coverage only. Primary regulatory data comes from the US Federal Register, US Congress, and EU Eurostat. UK AI regulation, APAC jurisdictions (China's AI Measures, Singapore's Model AI Governance Framework), and state-level US regulation are not directly covered.
• Research coverage is global but regulatory coverage is not. ArXiv and Semantic Scholar return globally authored papers. The regulatory scoring reflects US federal and EU sources only, which may understate governance activity in other jurisdictions.
• Scoring reflects publicly available signals. The framework alignment and regulatory velocity scores are derived from public documents and open data. Internal compliance programs, private regulatory correspondence, and enforcement settlements under seal are not visible.
• Data is fetched live at query time. There is no caching. If Federal Register or GitHub APIs are temporarily unavailable, the affected data source returns an empty array and the score for that component will be lower than the true value. Retry the tool if results look unexpectedly sparse.
• The research-regulation gap score is directional, not definitive. A CRITICAL_GAP score means research is publishing faster than regulations are adopting — it does not mean your organization is non-compliant. Legal compliance is a separate determination.
• audit_tooling_assessment takes 30-90 seconds. It queries all 8 sources in parallel with 120-second individual timeouts. Plan for this latency in time-sensitive agent workflows. Use targeted single-source tools for faster responses.
• Tool responses are capped. Each tool returns up to 15-25 items per data source to keep response sizes manageable. High-volume queries (e.g., broad topics like "AI" without a specific subtopic) may have relevant results beyond the cap.
• Not a substitute for legal counsel. This server provides regulatory intelligence and governance scoring for research and monitoring purposes. Compliance decisions, legal risk assessments, and regulatory filings require qualified legal professionals.

Integrations

• Zapier — trigger ai_regulatory_landscape on a schedule and push Regulatory Velocity Index changes to Slack, email, or a GRC ticket system
• Make — build governance monitoring workflows that run legislation_tracker weekly and update a Google Sheet with AI bill advancement status
• Google Sheets — export audit_tooling_assessment results to a governance scorecard that tracks composite score changes over time
• Apify API — call MCP tools programmatically from compliance pipeline scripts in Python or JavaScript for automated governance report generation
• Webhooks — send alerts when a scheduled ai_regulatory_landscape run returns a velocity level of RAPID or ACCELERATING
• LangChain / LlamaIndex — connect this MCP server as a tool provider in LangChain agent chains for autonomous AI governance research workflows

Troubleshooting

• Tool returns empty signals and a score of 0. This usually means one or more upstream actors returned no data. The most common cause is a query term that produced no results in the Federal Register or Congress search. Try broadening the topic parameter — use "artificial intelligence" instead of a narrow sub-topic. If the issue persists, the upstream actor may be temporarily unavailable; retry after a few minutes.

• Composite score seems lower than expected. The research-regulation gap component is inverted in the composite formula — a CRITICAL_GAP (high gap score) reduces the composite score. This is intentional: a domain where research has far outpaced regulation is treated as a governance risk. Check the researchRegGap.gapLevel field to see whether this component is driving a lower composite.

• risk_tier_classification returns PARTIAL alignment for a well-regulated AI use case. The tool scores framework alignment based on keyword matches in publicly available documents. If your use case's regulations use technical synonyms (e.g., "automated decision-making" instead of "artificial intelligence"), try including those synonyms in the useCase parameter.

• Response takes longer than 90 seconds. The audit_tooling_assessment tool queries all 8 sources in parallel, but individual actor response times vary. Federal Register and GitHub queries are typically fastest; ArXiv can be slower under high load. Each actor has a 120-second individual timeout. If a timeout is hit, that source returns an empty array — the tool still completes and returns results from the remaining sources.

• Spending limit reached error. If you see "error": true, "message": "Spending limit reached", your Apify run has hit the configured spending ceiling. Increase the maximum spend limit in your Apify run settings or use a smaller set of tools per session.

Responsible use

• This server queries publicly available government databases, academic repositories, and open-source code repositories.
• Federal Register and congressional bill data is US government-produced public information. ArXiv preprints and Semantic Scholar papers are publicly accessible research.
• Do not use governance assessment outputs as the sole basis for legal compliance representations to regulators, boards, or counterparties without independent legal review.
• Respect the terms of service of the underlying data sources accessed through this server.
• For guidance on web scraping and data access legality, see Apify's guide.

FAQ

How many AI model governance tools can I call in one session? There is no session limit. Each tool call is independent and costs $0.045. A full governance audit running all 8 tools costs $0.36 total. Apify's free plan covers 111 tool calls ($5 credit) before any payment is needed.

Does AI Model Governance MCP Server cover the EU AI Act specifically? Yes. The risk_tier_classification and compliance_gap_analysis tools match against 6 EU AI Act keyword patterns including "eu ai act", "high-risk ai", "general-purpose ai", "gpai", and "foundation model". Regulatory velocity scoring also tracks EU regulatory activity via Eurostat. Coverage is signal-based — the server detects EU AI Act mentions in public documents rather than maintaining a static rule database.

How is AI Model Governance MCP Server different from Credo AI, Fairly AI, or TruEra? Those platforms provide AI governance dashboards with proprietary testing frameworks for evaluating your own AI models. This server provides regulatory intelligence — tracking what governments and regulators are doing, how fast the regulatory environment is moving, and where the gaps between research and regulation lie. It integrates into AI agent workflows via MCP rather than requiring a standalone dashboard login. Cost is also different: $0.045 per query versus $25,000+ annual subscriptions.

How current is the regulatory data? Data is fetched live at query time from the Federal Register API, Congress.gov API, ArXiv API, and Semantic Scholar API. There is no caching layer. Results reflect the current state of each source's public API at the moment of the call.

Can I track AI regulatory changes for a specific AI use case over time? Yes. Schedule the risk_tier_classification or ai_regulatory_landscape tool to run weekly with consistent useCase or topic parameters. Export results to a dataset or Google Sheet to track Framework Alignment Score and Regulatory Velocity Index changes over time. Apify's scheduling feature supports cron-style intervals.

What jurisdictions does AI model governance intelligence cover? Primary coverage is US federal (Federal Register, Congress) and EU (Eurostat, EU AI Act signals). Research coverage via ArXiv and Semantic Scholar is global. State-level US regulation (California, Colorado, Illinois AI-specific laws) and non-US/EU jurisdictions (UK, China, Singapore) are not directly covered by the regulatory scoring components.

Is the AI governance scoring legally defensible for compliance reporting? No. The scores are intelligence and monitoring tools based on publicly available signal detection. They are suitable for internal governance team tracking and executive briefing, not for legal compliance certifications, regulatory filings, or representations to investors. Engage qualified legal counsel for compliance determinations.

Can I use AI model governance intelligence in a LangChain or LlamaIndex agent? Yes. The server implements the MCP Streamable HTTP transport specification. Any framework that supports MCP tool calling — including LangChain, LlamaIndex, and custom agent frameworks — can call tools via POST to https://ai-model-governance-mcp.apify.actor/mcp with a Bearer token. See the Apify MCP integration docs for framework-specific setup.

How does the research-regulation gap score work? The gap score measures how far AI research has outpaced regulation. It computes a research-to-regulation ratio (ArXiv + Semantic Scholar papers divided by Federal Register + Congress items), counts papers covering 8 frontier topics not yet well-regulated (foundation models, LLMs, diffusion, multimodal, agents, autonomous systems, RLHF, alignment), and counts papers published in 2025 or later as a recency signal. A CRITICAL_GAP means science is far ahead of policy — historically a leading indicator of upcoming regulatory activity.

What happens if one of the 8 data sources is temporarily unavailable? Each upstream actor call has a try/catch wrapper. If an individual actor fails or times out, it returns an empty array and the tool continues with the remaining sources. The affected scoring component will be lower than the true value. The tool still returns a complete response — it does not fail entirely due to one source being unavailable.

Can I combine this with other MCP servers in a multi-server agent setup? Yes. Most MCP clients support multiple simultaneous server connections. Pair this server with a broader regulatory monitoring MCP for non-AI sectors, or with a company research MCP to combine AI governance intelligence with specific company profiles when investigating AI enforcement actions.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.