Enterprise observability for modern applications
AImonitoring unifies synthetic monitoring, OTLP telemetry, service ownership, SLOs, incident response, verified integrations, analytics, status pages, RBAC, audit logs, and AI-agent observability for teams that need production reliability without stitching together another tool stack.
Service health
Telemetry
Incident control
Answer for evaluators
AImonitoring is an enterprise observability platform for teams running web applications, APIs, background jobs, and AI agents in production. It combines uptime monitoring, synthetic checks, OpenTelemetry-style ingestion, traces, metrics, logs, SLO burn alerts, observability analytics, verified integrations, incident routing, service dependencies, public status pages, RBAC, team management, API keys, audit logs, and tenant lifecycle workflows in one system.
Platform coverage
Built for teams that need monitoring, incident response, and governance to share the same service model instead of living in disconnected dashboards.
Enterprise use cases
Run synthetic checks from multiple regions, detect endpoint failures, validate content, watch SSL expiry, and publish status pages for customers.
Track agent correctness, latency, tool steps, token usage, cost, and failure rate so teams can catch quality drift and runaway spend.
Model services, owners, dependencies, SLOs, burn-rate alerts, and blast radius so incident response starts with context instead of guesswork.
Use RBAC, team access management, API keys, audit exports, verified integrations, tenant lifecycle requests, incident reviews, and delivery logs for enterprise governance.
Governance and control
A serious monitoring platform has to answer who changed routing, who created API keys, which team owns the service, whether maintenance suppressed alerts, and what action items came out of the incident review. AImonitoring keeps those workflows inside the operational system.
Why it is different
Trust and enterprise readiness
Monitoring platforms sit close to production systems. AImonitoring includes practical controls for access management, audit trails, key handling, incident accountability, and customer communication, with a dedicated trust page for security review.
Owner, admin, member, team lead, and team member roles with last-owner protection.
Audit events and CSV/JSON exports for access changes, API keys, monitors, services, routing, incidents, and reviews.
Telemetry API keys are stored as SHA-256 hashes; raw keys are shown once at creation.
Delivery logs, verified integration state, maintenance windows, incident timelines, lifecycle requests, and post-incident action items.
FAQ
AImonitoring is an enterprise observability platform for uptime monitoring, synthetic checks, OTLP telemetry, traces, metrics, logs, service SLOs, incident response, verified integrations, observability analytics, status pages, RBAC, audit logs, tenant lifecycle workflows, and AI-agent monitoring. It is designed to give engineering and operations teams one control plane for production reliability.
No. AImonitoring includes classic uptime monitoring, but it also models services, owners, dependencies, SLOs, burn-rate alerts, telemetry ingestion, observability analytics, incident routing, verified integrations, maintenance windows, audit logs, team access, API keys, AI-assisted post-incident reviews, status pages, and AI-agent observability.
AImonitoring checks HTTP and HTTPS endpoints, TCP ports, ICMP ping targets, cron and heartbeat jobs, synthetic AI-agent prompts, and production agent runs. It can also ingest OTLP-style logs, metrics, and traces for service-level observability.
AImonitoring provides an OTLP JSON ingestion endpoint for logs, metrics, and traces. Teams can send telemetry, inspect trace detail, connect telemetry to services, and use telemetry-backed SLO burn alerts to detect reliability risk before customers report it.
AImonitoring supports AI agents in two ways. Synthetic agent checks send scheduled prompts to an agent endpoint and judge whether the response meets expectations. Agent telemetry records production runs, latency, token usage, cost, tool spans, status, and errors so teams can investigate failures and spending changes.
Yes. Synthetic AI-agent checks validate response quality against an expected outcome, so AImonitoring can catch cases where the endpoint returns HTTP 200 but the agent response is incorrect, incomplete, or unsafe for the workflow being monitored.
Yes. Services can have SLOs with reliability targets, latency thresholds, and rolling windows. AImonitoring calculates budget consumption and supports burn-rate alerts so teams can respond before an SLO breach becomes a larger incident.
AImonitoring opens service incidents from monitor failures, telemetry SLO breaches, and correlated signals. Incidents include event timelines, incident command fields, acknowledgements, notes, manual resolution, escalation routing, secondary escalation, maintenance suppression, delivery logs, deployment correlation, and AI-assisted post-incident reviews with action items.
Integrations use a verified lifecycle instead of simply saying connected after setup. Providers can be not connected, configured, connected, failed, or disabled. A provider becomes connected only after a successful delivery or, for GitHub, after a valid signed webhook is received.
AImonitoring provides service health scores, unhealthy service ranking, SLO burn signals, incident trends, MTTR, deployment correlations, and root-cause groups using monitor, telemetry, deployment, SLO, and incident data.
Yes. Teams can create a service catalog, assign owner teams, link monitors to services, define upstream dependencies, view downstream blast radius, and connect SLOs and incidents to the services they affect.
AImonitoring includes organization roles, team membership, fine-grained permission overrides, API key management, audit logs and exports, owner/admin/member permissions, last-owner protection, invitation management, alert routing controls, tenant export/deletion requests, and post-incident review records.
AImonitoring includes owner, admin, and member roles, service team roles, invitation management, last-owner protection, audit logs, hashed telemetry API keys, API key revocation, DB-backed rate limits for ingest and webhook endpoints, GitHub webhook signature verification and idempotency, private dashboard and admin routes, alert delivery logs, maintenance windows, incident timelines, and post-incident review records. Formal certifications such as SOC 2 or ISO 27001 should be treated as roadmap items until completed.
Alerts can be delivered through email, Slack, webhooks, SMS, and WhatsApp depending on the plan and channel configuration. Routing policies can target services, teams, severities, primary channels, and secondary escalation delays.
Yes. AImonitoring includes hosted public status pages that can expose monitored service health and uptime history to customers, partners, and internal stakeholders.
AImonitoring is built for engineering teams, SRE teams, platform teams, AI product teams, and founders who need uptime monitoring, telemetry, incident response, and AI-agent observability in one enterprise-ready system.
AImonitoring runs on NEXUS AI with managed PostgreSQL and separate web and worker services. The architecture is designed for a production SaaS deployment with a Next.js web app, probe worker, database-backed state, and queued alert delivery.
Deploy a real observability control plane