Predictive Intelligence
Know issues before they happen
LSTM neural networks and gradient boosting models analyze hundreds of signals - predicting downtime 24-72 hours ahead, distinguishing bot attacks from viral traffic, and detecting SEO ranking drops weeks before they hit.
Prediction Alert
Generated 4 hours ago
Memory exhaustion predicted
Based on current growth patterns, your API server will reach 95% memory in ~6 hours.
Recommended actions:
- Scale horizontally to 3 replicas
- Review memory-heavy endpoints
- Enable auto-scaling rule
Traffic Intelligence
Not all spikes are attacks
Isolation Forest and Random Forest models analyze behavioral patterns to distinguish threats from opportunities in real-time.
Bot Attack Identified
Analysis: 96% bot probability. Uniform request timing (0.3s intervals), no mouse movement, linear scroll patterns, single user-agent across 2,400 sessions.
Action: Rate limiting applied automatically. Origin protected.
Viral Traffic Confirmed
Analysis: 94% legitimate. Referrer concentration (reddit.com 78%), human-like sessions (avg 3.2 pages, 45s duration), diverse user agents.
Action: No intervention needed. Enjoy the traffic.
99%+ bot detection accuracy without CAPTCHAs - using mouse movements, scroll behavior, keystroke dynamics, and session fingerprinting.
Capabilities
Four tiers of intelligence
Infrastructure Intelligence
Downtime Prediction
LSTM time-series models predict outages 24-72 hours ahead by analyzing response time trends and error rate patterns.
SSL/DNS Forecasting
Changepoint detection predicts certificate chain issues and DNS propagation problems before browsers reject them.
Performance Curves
Seasonal decomposition (STL) detects cyclical patterns like "slow every Thursday 2-4 PM" and correlates with deployments.
Capacity Exhaustion
Trend analysis on disk, memory, and connections with projected time-to-failure: "Pool exhaustion in 18 hours."
SEO Intelligence
Ranking Prediction
Gradient boosting predicts organic traffic changes 2-4 weeks ahead using ranking momentum, CTR trends, and competitor velocity.
Algorithm Detection
Bayesian changepoint detection spots Google updates before official announcement. Know if you're affected before SEO Twitter.
Opportunity Scoring
ML ranks pages by optimization ROI. "Page ranks #8, probability of #3 is 45%, predicted lift: +320 visits/month."
Cannibalization
Clustering algorithms detect pages competing against each other. "3 pages at #7, #12, #15. Consolidate for predicted #4."
Behavioral Intelligence
Funnel Prediction
Real-time session scoring predicts abandonment. "73% risk - 3 hesitation events on pricing, compared shipping 4x."
Rage Click Detection
Spatial clustering identifies UX problems. "Submit button rage-clicked by 23% of users. Response delay: 2.3s."
Session Quality
Every session gets a quality score. Segment users, identify high-value visitors, see patterns that correlate with conversion.
Bot vs Human
Ensemble classifiers detect bots without CAPTCHAs using mouse movement, scroll patterns, and session fingerprints. 99%+ accuracy.
Autopilot Intelligence
Smart Remediation
ML decides when to auto-fix vs escalate. "Cache purge auto-executed. Similar to 12 previous incidents. 94% success rate."
False Positive Suppression
Learn what's noise vs signal. "Alert suppressed: occurs every 02:00 during backup. 45 previous occurrences. Zero impact."
Third-Party Correlation
Connect your issues to external status. When Stripe has problems, we correlate it to your checkout failures automatically.
Personalized Thresholds
Static thresholds miss context. Our models learn your normal - and what deviations actually matter for your infrastructure.
Causal Intelligence
Root cause in seconds
Granger causality models connect signals across your stack to identify the actual source of problems - not just symptoms.
3 pages affected
1,234 users impacted
Cloudflare: Investigating
Status page updated
89% correlation
Granger causality confirmed
Instead of chasing symptoms across dashboards, see the complete causal chain from origin to impact.
Under The Hood
The models behind the predictions
Not a black box. Here's what's actually running.
LSTM + Transformer
Long Short-Term Memory networks analyze sequential patterns. Transformer attention catches long-range dependencies in your metrics.
Gradient Boosting
XGBoost and LightGBM for failure classification and ranking forecasts. Feature importance reveals which signals drive predictions.
Isolation Forest
Unsupervised detection of outliers in traffic patterns and system metrics. No labeled data required - learns your normal.
Bayesian Detection
Identifies when distributions shift - catching algorithm updates, infrastructure changes, and behavioral shifts as they happen.
Granger Causality
Distinguishes correlation from causation. When CDN latency spikes, does it actually cause cart abandonment? We test the relationship.
Ensemble Classifiers
Random Forest + Neural Network ensembles for bot detection and session scoring. Multiple models vote to reduce false positives.
Downtime warning
Predict outages days before they happen.
Prediction accuracy
Continuously improving with every outcome.
Fewer false alerts
ML learns what's noise vs signal for your site.
Bot detection
Without CAPTCHAs or user friction.
Stop reacting
Start predicting
See what's coming before it arrives. Let intelligence guide your operations.
Get Started