Hundreds of use cases. One platform.

Predict how much in fraudulent losses each account will experience in the next 30 days using relational patterns across the transaction graph.

Identify which accounts will experience an unauthorized login in the next 14 days from login patterns, device changes, and behavioral anomalies.

For each flagged account, predict which other accounts it will transact with - exposing coordinated fraud rings through link prediction.

Predict how many fraud-related chargebacks each merchant will generate, helping avoid $25K-$100K/month in fines.

For each cash deposit, detect whether the pattern indicates structuring to evade reporting thresholds.

For alerts with missing fraud type, predict which category they belong to - saving thousands of analyst hours per year.

For each high-risk account, rank which counterparties need review first to process 2x more SARs.

Determine if SMS verification actually stopped fraud or if the transaction would have failed anyway - reducing false declines 25%.

Predict which fraud typologies each account will be exposed to next month for 30-50% better fraud prevention.

Among accounts with recent deposits, predict which will make rapid withdrawals - with 50-70% fewer false positives.

Identify accounts that will move >$100K or execute >30 cross-border wires, saving 8,000+ investigator hours.

Predict which accounts will send large wires to sanctioned-country beneficiaries, avoiding $500K-$10M OFAC penalties.

For each cardholder, predict if the single largest transaction will exceed $10K - saving $10M+ annually.

Predict which accounts that purchased crypto will file a fiat chargeback, preventing $2M+ in irreversible losses per quarter.

Identify which wallets will experience abnormally large outflows before the compromise occurs.

Predict which addresses will receive funds from ransomware wallets, enabling deposits to be frozen before tainted funds arrive.

Among accounts with escalating deposits, predict which will deposit again - the average victim loses $150K before detection.

Identify which token contracts will experience sudden liquidity removal to protect retail investors proactively.

Predict which addresses will transact with dark web marketplace wallets to support FinCEN SAR filings.

Identify which addresses will send funds to sanctioned exchanges - OFAC penalties reach $10M per violation.

Predict which addresses will swap into sanctions-evasion stablecoins to demonstrate compliance maturity.

Identify which addresses will route funds through mixing services - an OFAC violation to transact with services like Tornado Cash.

Predict demand at the SKU-store-week level by connecting promotions, seasonal patterns, pricing, and supplier constraints.

Optimize stock levels across locations by learning from demand patterns, lead times, and supply chain relationships.

Forecast staffing needs from historical demand, seasonal patterns, event schedules, and operational constraints.

Predict infrastructure and resource requirements by learning from usage trends and growth patterns.

Forecast seasonal demand shifts by learning from multi-year patterns, promotional calendars, and external event data.

Predict optimal budget distribution across channels, campaigns, and regions by learning from historical spend-to-outcome relationships.

Learn from the full relational graph - purchases, views, returns, reviews - producing recommendations that reflect genuine preference.

Personalize content feeds, articles, and media by learning from interaction patterns across users and content items.

Re-rank search results using relational context - what similar users clicked, purchased, and returned.

Predict the most relevant offer for each customer based on purchase history, preferences, and behavior context.

Optimize email content, timing, and product selection per recipient using relational engagement signals.

Recommend newly launched items to users based on purchase history and relational context - even with zero interaction data on the new products.

Rerank push notifications and in-app messages by predicted engagement - which message, for which user, at which moment.

Predict the optimal send time for each user based on historical open and conversion patterns across channels.

Score leads using the full relational graph - CRM, product usage, support, and marketing interactions - not just company size and job title.

Predict which accounts are most likely to convert by learning from firmographic data, engagement patterns, and cross-account relationships.

Find prospects that resemble your best customers using relational similarity - beyond demographic overlap.

Predict individual purchase probability using transaction history, browsing behavior, and product interaction patterns.

Attribute conversions across touchpoints by learning the relational paths that actually drive outcomes.

Identify which customers are most likely to refer others based on satisfaction signals, social connections, and engagement depth.

Predict which free trial users will convert to paid by learning from product usage, feature adoption, and onboarding patterns.

Detect compound signals - declining usage, shrinking order size, unresolved tickets - weeks before customers leave.

Identify which churned customers are most likely to return and which re-engagement strategy will work for each.

Predict engagement depth from product usage, support interactions, community activity, and feature adoption.

Predict which rewards, tiers, and incentives drive retention for each customer segment.

Forecast subscription renewals by learning from usage patterns, support history, and account relationships.

Combine product usage, support interactions, billing status, and engagement trends into a single predictive health score per account.

Predict which dormant users are most likely to return - and what offer or message will bring them back.

Predict which products or tiers a customer will upgrade to based on usage patterns and peer behavior.

Predict which campaigns, messages, and creatives will resonate with each audience segment.

Determine the optimal outreach channel - email, push, in-app, SMS - for each customer and context.

Predict the best time to reach each customer based on historical response patterns and activity cycles.

Rank accounts and opportunities by likelihood to close, helping reps focus on the highest-value actions.

Identify which promotions will re-engage each user, which products to feature, and what discount depth maximizes ROI.

Personalize store vs. delivery vs. pickup pathways by learning from cross-channel behavior and preference signals.

Predict individual LTV from transaction history, product affinity, support experience, and loyalty behavior.

Forecast revenue at the account, segment, or company level using relational signals across your business data.

Predict price elasticity per customer and product using transaction, competitive, and inventory context.

Identify high-potential markets and segments by learning from geographic, demographic, and behavioral patterns.

Predict which funnel steps, page layouts, and CTAs will convert for each visitor segment.

Predict complementary items that increase average order value using co-purchase patterns and session context.

Predict staple depletion and trigger timely reminders. Learn individual purchase cadences from transaction history.

Match customer identities across databases, channels, and touchpoints using relational structure - not just fuzzy string matching.

Find and merge duplicate records in CRM, ERP, and master data systems using graph-based similarity.

Link records across systems - transactions to customers, events to entities - using learned relational patterns.

Identify household and family relationships across individual records for unified customer views.

Merge duplicate accounts across CRM, billing, and support systems using relational signals beyond name and email matching.

Link user activity across devices and browsers using behavioral and relational patterns - without relying on third-party cookies.