Building an AI Regulatory Compliance Agent with n8n

An r/AiAutomations post documented a 9th n8n project: a daily AI compliance agent that pulls EU AI Act and GDPR updates via SerpAPI, summarizes with Groq, generates a compliance report, and logs to Google Sheets. The OP built it on a phone, late at night after work. The pattern is generalizable to any regulatory domain.

The economic argument

A single missed regulation can cost a company millions in fines. Most companies pay consultants or paralegals $3,000+/mo to monitor manually — slow and gappy. A daily n8n agent costs $30-60/mo of API plus LLM spend and runs at dawn every weekday.

The shape of the workflow

Cron at 7 AM. Load keyword set (EU AI Act amendments, GDPR enforcement, HIPAA AI guidance, SOC 2 changes, sector-specific rules). Loop: per keyword, search via Scavio, optionally pull Reddit for analyst commentary, summarize with Groq or Claude, classify risk level, append to compliance report. End: email the report to the compliance lead and log every item to Sheets for audit.

Why swap SerpAPI for Scavio in this build

SerpAPI works fine for the original; the OP's flow is a clean shape. Scavio replaces SerpAPI at lower per-query cost ($0.0043 vs $0.005-$0.01 depending on tier) and adds a Reddit endpoint that catches drafts and analyst threads that pure SERP misses. Reddit threads on r/legaltech and r/europrivacy frequently flag draft EU AI Act amendments 1-2 weeks before mainstream legal news.

import os, requests
API_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': API_KEY}

KEYWORDS = [
    'EU AI Act amendments 2026',
    'GDPR enforcement actions 2026',
    'HIPAA AI guidance',
    'SOC 2 type II changes 2026'
]

def daily():
    out = {}
    for k in KEYWORDS:
        s = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
            json={'query': k}).json()
        r = requests.post('https://api.scavio.dev/api/v1/reddit/search', headers=H,
            json={'query': k}).json()
        out[k] = {
            'serp': s.get('organic_results', [])[:5],
            'reddit': r.get('posts', [])[:5]
        }
    return out

The LLM step

Pass the per-keyword results to Groq (Llama-3.1-70B) or Claude Sonnet 4.6. Prompt: "Summarize regulatory updates for X. Tag each item Low/Medium/High risk. Flag anything that requires action this quarter." Output JSON for downstream nodes.

The audit log

Append every item to Google Sheets with columns: date, keyword, source, summary, risk_level, action_required, due_date. The Sheets log is your compliance audit trail when regulators ask "how did you know about this update?"

What this replaces

A part-time paralegal at $40/hour × 10 hours/week = $1,600/mo of labor for the same monitoring function. The agent costs $30-60/mo of API plus Groq/Claude tokens — typically $50-100/mo all in. The compliance lead spends 10 minutes reviewing the daily email instead of 2 hours doing manual searches.

The honest scope

The agent surfaces signals. It does not replace legal review. For high-stakes decisions, a human compliance officer reads the flagged items and decides what action to take. The agent's job is to never miss something, not to decide what matters.

Generalizing beyond compliance

The same workflow shape works for any "daily updates per keyword" job: brand monitoring, competitive intelligence, news for a vertical newsletter, regulatory monitoring for a different domain. n8n handles the orchestration, Scavio handles the data, Groq or Claude handles the reasoning.