Real-Time Observability
Lynceus unifies metrics, logs, traces, profiles, events, topology, security, and business KPIs on one platform. Prometheus governs AI inference monitoring. GEOM-db provides the data foundation for cross-modality observability queries.
The Observability Problem
Ten monitoring tools replaced by one observability platform
Most organizations run 5-10 monitoring tools — Datadog for metrics, Splunk for logs, Jaeger for traces, Sentry for errors, PagerDuty for alerting, Grafana for dashboards — and a team to maintain the integrations between them. When an incident happens, the investigation starts by correlating timestamps across five tools that do not agree on what time it is.
Observability means joining metrics to logs to traces to topology to deployments in real time — so when a latency spike hits, one query returns the metric, the affected traces, the log entries from the failing service, and the deployment that preceded the spike.
Experience that grows with your scale
Monitoring Tools Replaced
Datadog, Splunk, Jaeger, Sentry, PagerDuty, Grafana
Experience that grows with your scale
Query Latency P99
Cross-domain joins
Experience that grows with your scale
Data Retention
Security logs, configurable per type
Experience that grows with your scale
Alert Accuracy
Causal correlation, not threshold
Ten Observability Domains
One platform replaces ten separate monitoring tools. Every signal type is a first-class citizen.
- MetricsTime-series metrics with sub-millisecond ingestion. Hot data in Redis, warm in ClickHouse, cold in S3. Downsampling over time.
- LogsStructured and unstructured logs with full-text search. Log-to-trace correlation. Log-to-metric correlation.
- TracesDistributed tracing with OpenTelemetry. Trace-to-log and trace-to-metric correlation. Service dependency maps.
- ProfilesContinuous profiling — CPU, memory, lock contention, I/O. Flame graphs and call tree analysis.
- EventsChange events, deployment events, configuration changes — correlated with metrics and traces.
- TopologyService map, infrastructure map, dependency graph — auto-discovered from traces and metrics.
How Babadook Products Deliver Observability
Lynceus observes. Prometheus governs AI. GEOM-db provides the data foundation.
Lynceus: Ten Domains
Metrics, logs, traces, profiles, events, topology, uptime, security, real user monitoring, and business KPIs — all on one platform with one query language.
Prometheus: AI Observability
Model drift, inference latency, token cost, guardrail violations, GPU health — monitored in real time. Every AI inference causally traced.
GEOM-db: Data Foundation
Cross-modality queries join time-series metrics to graph topology to relational deployment data — in one SQL statement. No ETL pipeline between observability tools.
Haephestus: DevOps Visibility
CI/CD pipeline monitoring, deployment tracking, and infrastructure change audit — all on the same platform as production observability.
Query Latency Distribution
99% of queries complete under 2ms. The long tail is where the interesting problems live — the 0.1% of queries over 50ms are the ones that tell you what is broken. Most monitoring tools average their latency and hide the tail. Lynceus shows you the full distribution so you can find the outliers before they become incidents.
The Observability Stack
One platform, ten domains, one query language — from infrastructure to business KPIs.
Lynceus
Ten observability domains on one platform. 99% of queries under 2ms. Causal correlation, not threshold alerting. Self-hosted, managed, BYOC, or hybrid edge.
Prometheus
AI-specific observability — model drift, inference latency, guardrail violations, GPU health. Every inference causally traced and cryptographically signed.
GEOM-db
Cross-modality data foundation — join time-series to graph to relational in one SQL statement. No ETL between observability tools.
Ten Observability Domains
One platform replaces ten separate monitoring tools. Every signal type is a first-class citizen.
Real-Time Observability FAQ
Architecture to delivery
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