Source Collection
Regulatory feeds, bulletins, and institutional postings are continuously monitored across configured geographies.
Platform Architecture
Built for reliability, trust, and product execution speed.
Recall Radar provides a dependable safety-event layer for teams building consumer and regulated product experiences. The platform is designed for operational clarity from ingestion to user notification.
Event Pipeline
One alert protocol across many sources
Different agencies publish in different ways. Recall Radar unifies those formats into a stable integration contract so your product can ship a coherent user experience.
Event Normalization
Notices are parsed into a unified schema with product taxonomy, region mapping, severity, and source metadata.
Risk Classification
Rules and scoring logic prioritize high-impact events so operations teams can route alerts with confidence.
Delivery Orchestration
Structured events are delivered to your backend for push, in-app safety banners, and user-facing timelines.
Operational Controls
Designed for safety-critical workflows
- Source verification metadata stored per event
- Deduplication logic for overlapping notices
- Severity and confidence indicators for escalation
- Structured output for backend and analytics pipelines
- Delivery status tracking for alert operations
- Configurable region and category filters
Reliability Focus
Engineered for consistent alerting
High-severity notices demand fast and dependable delivery. Recall Radar is built around uptime discipline, data quality checks, and clear operational observability.
99.95%
Platform availability objective
< 90 sec
Alert pipeline target latency
Launch Path
Practical rollout, enterprise-ready trajectory
Phase 1
Pilot Region
Launch with one market and core product categories to validate engagement and alert conversion.
Phase 2
Coverage Expansion
Add additional agencies and countries while preserving a single event contract for product teams.
Phase 3
Advanced Intelligence
Deploy predictive risk patterns and enhanced segmentation for high-value user communication.