Use Cases
Real-world AI Agent use cases from marketing to engineering, research to productivity — with recommended tool stacks and step-by-step guides
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Marketing
14
Engineering
7
Research
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Productivity
8
Industry
Automated Review of Key Legal Contract Clauses
Upload a contract PDF, and AI automatically identifies risky clauses, unequal terms, and missing key protective clauses, outputting an annotated report to help legal professionals improve contract review efficiency by 3-5 times.
Steps
- 1.Configure filesystem MCP to read the PDF contract
- 2.Write professional contract review prompts
- 3.Claude analyzes key clauses one by one
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Automated Financial Data Visualization Report
Connect Excel or databases, and let the AI Agent automatically generate monthly financial analysis reports: revenue trends, cost structure, profit analysis, anomaly alerts, presented with visual charts.
Steps
- 1.Configure database MCP to connect to the financial system
- 2.Define report templates and analysis dimensions
- 3.AI automatically generates SQL queries and analyzes data
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Automated Medical Literature Review Generation
Clinical researchers input a research question, and AI automatically searches PubMed/arXiv, filters high-quality literature, extracts research methods and conclusions, and generates a systematic review framework compliant with PRISMA guidelines, significantly accelerating early-stage research work.
Steps
- 1.Input research question in PICO format
- 2.AI searches PubMed and arXiv for relevant papers
- 3.Filter literature based on inclusion/exclusion criteria
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Financial Report Anomaly Detection
Connect company financial data to an AI Agent to automatically compare historical trends and industry benchmarks, identify abnormal indicators (such as a sudden surge in accounts receivable or a sharp drop in gross margin), and generate risk warning reports to assist financial analysts in decision-making.
Steps
- 1.Connect to PostgreSQL database to store financial data
- 2.Configure historical comparison and industry benchmark thresholds
- 3.AI automatically calculates key financial ratios
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AI-Driven Customer Success Workflow: Auto-Calculate Health Scores + Personalized Renewal Alerts
Integrate CRM data, product usage logs, and customer communication records. Use an AI Agent to automatically calculate customer health scores, identify high churn risk accounts, and generate personalized intervention suggestions and outreach email drafts for CSMs, reducing churn by 20-30%. ## Direct Answer **What problem does this workflow solve?** Two common pain points in B2B SaaS: 1. CSMs manage 50+ customers and don't know who to prioritize. 2. They only discover a customer is about to churn when it's too late to intervene. **What can AI do?** - Automatically calculate a health score (0-100) for each account daily. - Identify downward trends in health scores (2 consecutive weeks of decline → alert). - Generate personalized intervention suggestions for high-risk accounts. - Draft outreach emails (citing specific customer usage data). ## Health Score Calculation Model ```python def calculate_health_score(account_data): score = 100 # Login frequency (max deduction 30) login_days = account_data['login_days_last_30'] if login_days < 5: score -= 30 elif login_days < 15: score -= 15 # Core feature usage (max deduction 25) adoption = account_data['feature_adoption_rate'] if adoption < 0.3: score -= 25 elif adoption < 0.6: score -= 10 # Support tickets (more = lower satisfaction, max deduction 20) tickets = account_data['support_tickets_last_30'] if tickets > 10: score -= 20 elif tickets > 5: score -= 10 # Contract renewal approaching (max deduction 15) days_to_renewal = account_data['days_to_renewal'] if days_to_renewal < 30: score -= 15 return max(0, score) ``` ## n8n Workflow ``` Trigger daily at 08:00 ↓ HubSpot API → Pull all active accounts ↓ postgres MCP → Query product usage data from last 30 days ↓ AI Agent (Claude) → Calculate health scores + generate risk labels ↓ Filter accounts with health score < 60 ↓ AI Agent → Generate intervention suggestions + email drafts for each risk account ↓ Write to CRM + Send Slack notification to CSM ``` ## Measured Results (50-person SaaS company, 3 months) - High-risk customer identification advanced from "2 weeks before renewal" to "8 weeks before renewal" - CSM daily handled customers: 45 → 70 (same headcount) - Net Revenue Retention (NRR) increased from 98% to 103%
Steps
- 1.Define customer health indicators: login frequency, feature adoption rate, support ticket count, contract renewal time.
- 2.Configure HubSpot Trigger in n8n to pull account data daily.
- 3.Use postgres MCP to connect to the product usage log database.
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Real-Time AI Agent Risk Warning System for Financial Scenarios
In fintech platforms, massive colloquial user voice data serves as sensitive signals for fault warnings, but it easily leads to high false positives and alert fatigue. This solution, based on Ant Group's open-source TingIS system, implements end-to-end streaming risk warnings through five modules: semantic distillation, cascaded routing, event unification, memory management, and multi-dimensional noise reduction. The system achieves P90 latency ≤10 minutes, distribution accuracy 90%+, and suppresses over 94% of invalid alerts under a throughput of >2000 messages per minute, enabling efficient early fault warnings.
Steps
- 1.Deploy the TingIS system and configure the data collection layer to capture user complaint voice streams in real time.
- 2.In the semantic distillation module, use LLM to compress raw complaints into standardized short summaries and anonymize PII.
- 3.Build a cascaded routing mechanism: use keyword matching to ensure core business accuracy, and multi-vector retrieval to cover long-tail scenarios.
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