Automated Network Anomaly Detection in Software

Revenue Institute's AI learns the legitimate operational patterns specific to your stack - Datadog metrics, VPC flow logs, GitHub and Jira activity, and cloud-native signals from AWS, GCP, or Azure - then flags real deviations instead of every traffic spike. It knows the difference between a product release driving API traffic up and an actual intrusion, which is exactly the distinction generic SIEM tooling misses. That's what takes false positive volume down from the 60-70% range most teams live with toward single digits, so on-call engineers stop chasing noise and start catching real signal faster.

Yes. The model trains on your network telemetry and metadata, not your customers' application data, and every playbook runs with your engineering team's approval gates intact - nothing auto-remediates without sign-off unless you explicitly configure it to. Deployment respects your existing SOC 2 controls and data residency requirements; we build inside your compliance boundary, we don't ask you to expand it.

Deployment runs inside the first 100 days: weeks 1-2 cover infrastructure assessment and ingestion setup across Datadog, VPC flow logs, and your cloud provider's native signals; weeks 3-6 train the baseline model on your historical traffic; weeks 7-9 cover testing, playbook configuration, and on-call team training; weeks 10-14 are a phased go-live with active monitoring. A rollout like this is scoped to show measurable false-positive reduction within 60 days of production launch.

Four moving parts. Ingestion pulls traffic telemetry from Datadog, VPC flow logs, and your CI/CD and issue-tracking signals. Baseline learning watches that traffic long enough to know what normal looks like for your release cadence and infrastructure patterns. Detection scores deviations by business impact - a spike during a scheduled release scores differently than the same spike at 3am with no deploy in flight. Response runs through playbooks your engineering team pre-approves, so containment happens in minutes without paging someone for a false alarm.

By day 30, the system is ingesting traffic from your full stack and shadowing production - flagging anomalies without acting on them - so your team can check its calls against incidents you already know about. By day 60, it's running in production for a defined slice of your infrastructure, with engineers reviewing every flag and a measured baseline against your pre-deployment alert volume. By day 90, on-call is working from a risk-ranked queue instead of raw alert noise, false positive rates are stabilizing well below the original baseline, and you've decided which service or environment to bring in next.