{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_greip","slug":"greip","name":"Greip","type":"product","url":"https://greip.io","page_url":"https://unfragile.ai/greip","categories":["data-analysis","code-review-security"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_greip__cap_0","uri":"capability://data.processing.analysis.real.time.fraud.risk.scoring.with.sub.100ms.latency","name":"real-time fraud risk scoring with sub-100ms latency","description":"Greip processes incoming transaction requests through a multi-signal scoring engine that combines IP geolocation, device fingerprinting, and behavioral heuristics to assign a fraud risk score in under 100ms. The system evaluates transaction metadata (IP, device ID, user behavior patterns) against historical fraud patterns and returns a numerical risk score that integrates directly into payment authorization flows without blocking legitimate transactions.","intents":["I need to block high-risk transactions at checkout without slowing down the payment experience for legitimate users","I want to catch fraud before payment processing to avoid chargebacks and failed transactions","I need a fraud detection system that doesn't require me to build complex ML models in-house"],"best_for":["Early-stage fintech startups with moderate transaction volume (<100k/month)","Indie app developers monetizing through in-app purchases or subscriptions","SaaS platforms with payment processing but limited fraud engineering resources"],"limitations":["Sub-100ms latency assumes network round-trip time; actual end-to-end latency depends on client-side integration and network conditions","Scoring accuracy degrades on edge cases (new users, unusual geographies) where behavioral history is sparse","Free tier rate limits (typically 100-1000 requests/month) make it unsuitable for production use on platforms processing >1000 transactions/day"],"requires":["API key from Greip dashboard","HTTPS endpoint to receive transaction data","Client-side device fingerprinting library (JavaScript SDK or equivalent)","Transaction metadata: IP address, device ID, user ID, transaction amount, timestamp"],"input_types":["JSON transaction object with IP, device fingerprint, user ID, amount, timestamp","Device metadata from client-side fingerprinting"],"output_types":["JSON fraud risk score (0-100 scale)","Risk level classification (low/medium/high)","Detailed signal breakdown (IP risk, device risk, behavioral risk)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_greip__cap_1","uri":"capability://data.processing.analysis.ip.geolocation.and.proxy.vpn.detection","name":"ip geolocation and proxy/vpn detection","description":"Greip maintains a continuously-updated IP address database that maps IP ranges to geographic locations, ISP information, and flags suspicious IP characteristics (datacenter IPs, known proxy services, VPN exit nodes). When a transaction IP is queried, the system performs a lookup against this database and returns geolocation coordinates, country/city, ISP name, and risk flags indicating whether the IP belongs to a proxy, VPN, or datacenter network commonly used for fraud.","intents":["I want to detect when users are accessing my app from unexpected geographic locations to catch account takeovers","I need to identify transactions from proxy/VPN services that mask the user's real location","I want to flag datacenter IPs that are commonly used for bot attacks and fraud rings"],"best_for":["Payment processors needing geographic velocity checks (same user, multiple countries in short time)","SaaS platforms with geographic licensing restrictions","Platforms detecting account takeover via impossible travel patterns"],"limitations":["IP geolocation accuracy is ~95% at country level, ~70% at city level; rural areas and mobile networks have lower accuracy","VPN/proxy detection relies on known IP lists and heuristics; sophisticated residential proxies may evade detection","Database updates lag real-world IP changes by 24-48 hours; newly-issued IPs may not be classified","Cannot distinguish between legitimate VPN use (privacy-conscious users) and fraudulent VPN use without additional signals"],"requires":["API key from Greip","Valid IPv4 or IPv6 address as input","Network connectivity to Greip API endpoint"],"input_types":["IPv4 address (dotted decimal notation)","IPv6 address (colon-separated hexadecimal)"],"output_types":["JSON object with country, city, latitude, longitude, ISP, organization","Boolean flags: is_proxy, is_vpn, is_datacenter, is_residential_proxy","Confidence scores for each classification"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_greip__cap_2","uri":"capability://data.processing.analysis.device.fingerprinting.and.browser.device.identification","name":"device fingerprinting and browser/device identification","description":"Greip provides a client-side JavaScript SDK that collects device characteristics (user agent, screen resolution, installed fonts, canvas fingerprint, WebGL renderer, timezone, language settings) and generates a stable device fingerprint hash. This fingerprint is sent with transactions to enable device-level fraud detection, allowing the system to identify when multiple accounts are being accessed from the same device or when a device's behavior pattern suddenly changes.","intents":["I want to detect when multiple fraudulent accounts are being created from the same device","I need to identify account takeover when a legitimate user's account is accessed from a new device","I want to track device behavior over time to catch organized fraud rings using the same infrastructure"],"best_for":["Web-based SaaS platforms with browser-based checkout flows","Platforms with high account creation velocity where device clustering is a strong fraud signal","Marketplaces detecting organized fraud rings using shared infrastructure"],"limitations":["Device fingerprints are not 100% stable; browser updates, OS patches, and plugin changes can alter the fingerprint hash","Privacy-focused browsers (Firefox with enhanced tracking protection, Brave) intentionally randomize fingerprinting signals, reducing accuracy","Cannot fingerprint native mobile apps without additional SDK integration; JavaScript SDK is browser-only","Users clearing browser cache/cookies or using incognito mode will generate new fingerprints, creating false negatives","Canvas fingerprinting and WebGL detection may be blocked by browser security policies or privacy extensions"],"requires":["Greip JavaScript SDK embedded in checkout page or login form","Browser with JavaScript enabled","HTTPS connection (required for secure fingerprint transmission)","Server-side API key to validate fingerprint authenticity"],"input_types":["Client-side device characteristics collected by JavaScript SDK","Browser user agent string","Canvas and WebGL rendering data"],"output_types":["Device fingerprint hash (stable identifier for the device)","Confidence score for fingerprint stability","Device metadata (browser, OS, screen resolution, timezone)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_greip__cap_3","uri":"capability://data.processing.analysis.behavioral.anomaly.detection.via.transaction.pattern.analysis","name":"behavioral anomaly detection via transaction pattern analysis","description":"Greip analyzes transaction patterns for each user account (transaction frequency, amount distribution, time-of-day patterns, geographic velocity) and flags deviations from the user's historical baseline as behavioral anomalies. The system learns normal behavior from the first 10-20 transactions and then scores subsequent transactions based on how much they deviate from established patterns (e.g., a user who normally spends $50/transaction suddenly spending $5000 triggers a high anomaly score).","intents":["I want to detect account takeover when a compromised account suddenly shows unusual spending patterns","I need to flag transactions that deviate significantly from a user's historical behavior without blocking legitimate high-value purchases","I want to identify when a user's account is being used for money laundering or structuring (many small transactions to avoid thresholds)"],"best_for":["Platforms with established user bases where historical transaction data is available","Payment processors handling recurring transactions where baseline behavior is stable","Platforms with diverse user spending patterns where rule-based fraud detection is insufficient"],"limitations":["Requires minimum 10-20 historical transactions per user to establish baseline; new users have no behavioral history and cannot be scored","Seasonal spending patterns (holiday shopping, tax season) may cause false positives if not explicitly modeled","Legitimate behavior changes (user moving to new country, job change, large purchase) are indistinguishable from fraud without additional context","Behavioral models degrade over time if user behavior legitimately shifts; requires periodic retraining","Cannot detect sophisticated fraud that mimics normal behavior patterns (slow-and-low attacks)"],"requires":["Minimum 10-20 historical transactions per user in Greip system","Consistent transaction metadata (amount, timestamp, category, merchant)","User ID to correlate transactions across sessions"],"input_types":["Transaction history: amount, timestamp, merchant category, geographic location","User ID for baseline correlation"],"output_types":["Anomaly score (0-100 scale indicating deviation from baseline)","Specific anomaly type (amount anomaly, frequency anomaly, geographic anomaly, time-of-day anomaly)","Confidence score for anomaly detection"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_greip__cap_4","uri":"capability://tool.use.integration.api.based.transaction.risk.assessment.with.webhook.callbacks","name":"api-based transaction risk assessment with webhook callbacks","description":"Greip exposes a REST API endpoint that accepts transaction details (IP, device fingerprint, user ID, amount, merchant category) and returns a fraud risk assessment synchronously or asynchronously via webhook. The API supports both real-time blocking (synchronous response) and async scoring (webhook callback) to accommodate different integration patterns. Developers can call the API at transaction time, post-transaction for batch scoring, or set up webhooks to receive risk updates as new signals become available.","intents":["I want to integrate fraud detection into my payment authorization flow without adding latency","I need to score historical transactions in batch to identify fraud patterns I may have missed","I want to receive fraud alerts asynchronously when new risk signals emerge for existing transactions"],"best_for":["Payment processors with synchronous authorization flows requiring sub-100ms responses","Platforms with batch processing pipelines for historical transaction analysis","Teams wanting to decouple fraud detection from payment authorization for flexibility"],"limitations":["Synchronous API calls add network latency (typically 50-200ms depending on geographic proximity to Greip servers); cannot be eliminated","Webhook callbacks are not guaranteed delivery; requires client-side retry logic and idempotency handling","Rate limits on free tier (100-1000 requests/month) make production use infeasible for high-volume platforms","No built-in request queuing or batching; high-volume platforms must implement their own queue management"],"requires":["API key from Greip dashboard","HTTPS endpoint for webhook callbacks (if using async mode)","Transaction metadata in JSON format","Network connectivity to Greip API servers"],"input_types":["JSON transaction object: {ip, device_fingerprint, user_id, amount, merchant_category, timestamp}","Optional: user profile data, historical transaction context"],"output_types":["JSON fraud risk assessment: {risk_score, risk_level, signals, confidence}","Webhook payload with same structure for async callbacks"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_greip__cap_5","uri":"capability://tool.use.integration.freemium.tier.with.rate.limited.api.access.for.development.and.testing","name":"freemium tier with rate-limited api access for development and testing","description":"Greip offers a free tier that provides limited API access (typically 100-1000 requests/month) with full feature parity to paid tiers, enabling developers to test fraud detection against real transaction patterns before committing budget. The free tier includes all core capabilities (IP geolocation, device fingerprinting, behavioral analysis) but with strict rate limits enforced at the API key level. Developers can upgrade to paid tiers (typically $99-999/month) for higher rate limits and priority support.","intents":["I want to test fraud detection on my platform before committing to a paid plan","I need to validate that Greip's fraud signals are relevant to my specific use case and transaction patterns","I want to prototype a fraud detection system without upfront cost"],"best_for":["Early-stage startups with limited fraud detection budget","Indie developers building side projects with payment processing","Teams evaluating multiple fraud detection vendors before making a purchasing decision"],"limitations":["Free tier rate limits (100-1000 requests/month) are insufficient for production use on any platform processing >1000 transactions/month","No SLA or uptime guarantees on free tier; service may be deprioritized during outages","Limited documentation and support for free tier users; community support only","Free tier API keys may be rate-limited more aggressively than paid tiers during peak usage","Requires credit card to sign up; free tier may be revoked if account shows signs of abuse"],"requires":["Email address to create Greip account","Credit card for account verification (not charged for free tier)","Acceptance of Greip terms of service"],"input_types":["Account signup form with email and password"],"output_types":["API key for free tier access","Dashboard with usage metrics and fraud alerts"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_greip__cap_6","uri":"capability://data.processing.analysis.dashboard.with.fraud.alert.visualization.and.transaction.history","name":"dashboard with fraud alert visualization and transaction history","description":"Greip provides a web-based dashboard that displays real-time fraud alerts, historical transaction risk scores, and aggregated fraud metrics (fraud rate, high-risk transaction volume, geographic distribution of fraud). The dashboard allows developers to review flagged transactions, adjust risk thresholds, and export transaction history for analysis. Alerts are surfaced with risk scores, signal breakdowns, and recommended actions (block, challenge, allow).","intents":["I want to see which transactions were flagged as high-risk and understand why","I need to monitor fraud trends over time to adjust my risk thresholds","I want to export transaction data for analysis in my own BI tools"],"best_for":["Fraud analysts and compliance teams reviewing flagged transactions","Product managers monitoring fraud metrics to inform product decisions","Operations teams investigating fraud incidents and chargebacks"],"limitations":["Dashboard is read-only; cannot directly block/allow transactions from dashboard (requires API integration)","Real-time alert latency depends on dashboard refresh rate; may lag actual fraud detection by 5-30 seconds","Limited customization of dashboard views; cannot create custom fraud metrics or alerts","Export functionality may be limited to CSV; no direct integration with BI tools like Tableau or Looker","Dashboard access is tied to API key; no fine-grained role-based access control (RBAC) for team members"],"requires":["Greip account with active API key","Web browser with JavaScript enabled","HTTPS connection to Greip dashboard"],"input_types":["Transactions processed through Greip API"],"output_types":["HTML dashboard with interactive charts and tables","CSV export of transaction history","Real-time alert notifications (email or in-dashboard)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_greip__cap_7","uri":"capability://tool.use.integration.webhook.based.real.time.fraud.alerts.with.configurable.thresholds","name":"webhook-based real-time fraud alerts with configurable thresholds","description":"Greip sends webhook notifications to a developer-specified HTTPS endpoint whenever a transaction exceeds a configurable fraud risk threshold. Webhooks are sent in real-time (within seconds of transaction scoring) and include full transaction details, risk score, signal breakdown, and recommended action. Developers can configure separate thresholds for different actions (alert, block, challenge) and customize webhook payload format.","intents":["I want to be notified immediately when a high-risk transaction is detected so I can take action","I need to integrate fraud alerts into my existing incident management system (Slack, PagerDuty, email)","I want to trigger custom business logic (refund, account suspension, manual review) when fraud is detected"],"best_for":["Operations teams needing real-time fraud alerts for manual review","Platforms with custom fraud response workflows (e.g., automatic refunds for confirmed fraud)","Teams integrating fraud detection with incident management systems"],"limitations":["Webhook delivery is not guaranteed; Greip may retry failed webhooks 3-5 times but eventual delivery is not assured","Webhook payload size may be large (>10KB) for transactions with detailed signal breakdowns; may exceed some webhook consumers' limits","Webhook latency depends on Greip's server load and network conditions; typically 1-5 seconds but can be higher during peak usage","No built-in deduplication; if a transaction is re-scored, multiple webhooks may be sent for the same transaction","Webhook configuration is global; cannot set different thresholds for different merchant categories or user segments"],"requires":["HTTPS endpoint that accepts POST requests","Endpoint must respond with 2xx status code within 30 seconds","Ability to parse JSON webhook payload","Webhook secret for HMAC signature verification (recommended for security)"],"input_types":["Webhook configuration: threshold, endpoint URL, payload format"],"output_types":["JSON webhook payload: {transaction_id, risk_score, risk_level, signals, recommended_action, timestamp}"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["API key from Greip dashboard","HTTPS endpoint to receive transaction data","Client-side device fingerprinting library (JavaScript SDK or equivalent)","Transaction metadata: IP address, device ID, user ID, transaction amount, timestamp","API key from Greip","Valid IPv4 or IPv6 address as input","Network connectivity to Greip API endpoint","Greip JavaScript SDK embedded in checkout page or login form","Browser with JavaScript enabled","HTTPS connection (required for secure fingerprint transmission)"],"failure_modes":["Sub-100ms latency assumes network round-trip time; actual end-to-end latency depends on client-side integration and network conditions","Scoring accuracy degrades on edge cases (new users, unusual geographies) where behavioral history is sparse","Free tier rate limits (typically 100-1000 requests/month) make it unsuitable for production use on platforms processing >1000 transactions/day","IP geolocation accuracy is ~95% at country level, ~70% at city level; rural areas and mobile networks have lower accuracy","VPN/proxy detection relies on known IP lists and heuristics; sophisticated residential proxies may evade detection","Database updates lag real-world IP changes by 24-48 hours; newly-issued IPs may not be classified","Cannot distinguish between legitimate VPN use (privacy-conscious users) and fraudulent VPN use without additional signals","Device fingerprints are not 100% stable; browser updates, OS patches, and plugin changes can alter the fingerprint hash","Privacy-focused browsers (Firefox with enhanced tracking protection, Brave) intentionally randomize fingerprinting signals, reducing accuracy","Cannot fingerprint native mobile apps without additional SDK integration; JavaScript SDK is browser-only","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.893Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=greip","compare_url":"https://unfragile.ai/compare?artifact=greip"}},"signature":"NT90qHCTcy5xm/3v47i1DjyDz1mzDWpD+RDD/Zp+XdAyd8d0PlfuOj5SXwsD20peZ9YhrHyXd0DUt+N0D9vkCA==","signedAt":"2026-06-21T07:13:37.825Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/greip","artifact":"https://unfragile.ai/greip","verify":"https://unfragile.ai/api/v1/verify?slug=greip","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}