Build a Custom Observability Dashboard Using AI in Minutes
Describe your Observability Dashboard in plain English and create a production-ready build, from design and development to deployment, in minutes. No code needed.
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Emergent Key Features for Building an Observability Dashboard
Traces, logs and metrics joined on request identity, so an investigation follows evidence rather than timestamps.
Traces Joined to Logs
Log lines are linked to the request they belong to, so investigating a slow trace does not require searching by timestamp and hoping.
Span Level Attribution
Request time is broken into application, database and external calls, which locates the cause instead of confirming the endpoint is slow.
Error Grouping with Context
Occurrences are grouped by cause with affected request counts, so triage is by impact rather than by volume of log noise.
Service Dependency Mapping
Call volume and error rates are shown per dependency edge, revealing which downstream service is degrading your own.
Deployment Correlation
Behaviour before and after each release is compared, which usually identifies the cause of an incident faster than any other signal.
Tail Latency Focus
The 99th percentile is reported alongside the median, because the failures users report live in the tail rather than the average.
Observability Dashboard Use Cases You Can Build in Minutes
Follow a Slow Request to the Span That Caused It
Request traces across services with duration per span, the slowest spans in the period, traces exceeding your latency budget, a service dependency map with call volume and error rate per edge and full span breakdown per trace.
window.awbMockup = { distributedTrace: "Build an observability dashboard showing request traces across services with duration per span, the slowest spans in the current period, traces exceeding your latency budget, service dependency map with call volume and error rate on each edge, and the ability to open any trace to its full span breakdown.", errorAnalysis: "Build an error observability dashboard grouping errors by type, service and endpoint with occurrence counts, first and most recent occurrence, affected user or request count, errors new since the last deployment, stack trace detail for each group, and errors correlated with recent releases.", latencyBreakdown: "Build a latency dashboard showing request duration at median, 95th and 99th percentile by endpoint and service, time attributed to application, database and external calls within each request, endpoints whose latency worsened this week, and the specific downstream dependency contributing most delay.", deploymentImpact: "Build a release observability dashboard comparing error rate, latency percentiles and throughput before and after each deployment, showing which release coincided with a change in behaviour, services affected, time from deployment to first anomaly, and a rollback recommendation signal where metrics degraded."};
Triage Errors by Impact, Not by Volume
Errors grouped by type, service and endpoint with occurrence counts, first and most recent occurrence, affected user or request count, errors new since the last deployment, stack trace detail and correlation with recent releases.
window.awbMockup = { distributedTrace: "Build an observability dashboard showing request traces across services with duration per span, the slowest spans in the current period, traces exceeding your latency budget, service dependency map with call volume and error rate on each edge, and the ability to open any trace to its full span breakdown.", errorAnalysis: "Build an error observability dashboard grouping errors by type, service and endpoint with occurrence counts, first and most recent occurrence, affected user or request count, errors new since the last deployment, stack trace detail for each group, and errors correlated with recent releases.", latencyBreakdown: "Build a latency dashboard showing request duration at median, 95th and 99th percentile by endpoint and service, time attributed to application, database and external calls within each request, endpoints whose latency worsened this week, and the specific downstream dependency contributing most delay.", deploymentImpact: "Build a release observability dashboard comparing error rate, latency percentiles and throughput before and after each deployment, showing which release coincided with a change in behaviour, services affected, time from deployment to first anomaly, and a rollback recommendation signal where metrics degraded."};
Debug the Tail Your Users Actually Feel
Request duration at median, ninety fifth and ninety ninth percentile by endpoint and service, time attributed to application, database and external calls, endpoints that worsened this week and the dependency contributing most delay.
window.awbMockup = { distributedTrace: "Build an observability dashboard showing request traces across services with duration per span, the slowest spans in the current period, traces exceeding your latency budget, service dependency map with call volume and error rate on each edge, and the ability to open any trace to its full span breakdown.", errorAnalysis: "Build an error observability dashboard grouping errors by type, service and endpoint with occurrence counts, first and most recent occurrence, affected user or request count, errors new since the last deployment, stack trace detail for each group, and errors correlated with recent releases.", latencyBreakdown: "Build a latency dashboard showing request duration at median, 95th and 99th percentile by endpoint and service, time attributed to application, database and external calls within each request, endpoints whose latency worsened this week, and the specific downstream dependency contributing most delay.", deploymentImpact: "Build a release observability dashboard comparing error rate, latency percentiles and throughput before and after each deployment, showing which release coincided with a change in behaviour, services affected, time from deployment to first anomaly, and a rollback recommendation signal where metrics degraded."};
Find the Release Behind the Incident
Error rate, latency percentiles and throughput before and after each deployment, which release coincided with a change in behaviour, services affected, time from deployment to first anomaly and a rollback signal where metrics degraded.
Find the Release Behind the Incident
Error rate, latency percentiles and throughput before and after each deployment, which release coincided with a change in behaviour, services affected, time from deployment to first anomaly and a rollback signal where metrics degraded.
Build a custom observability dashboard in 4 simple steps
Correlate traces, logs and metrics in one view, so debugging a production problem starts with evidence rather than with guesswork.
Start with the questions you ask during an incident, such as which service is slow, which change caused it, which requests fail and what the logs said at that moment. Emergent builds around your service topology rather than a host list.
Link your tracing backend, log store and metrics source. Emergent correlates them on trace and request identifiers, so a slow endpoint leads directly to the span and the log lines behind it.
Ask to add a service, change a latency budget, group errors differently or bring another log source into the correlation, and the trace view rebuilds around it.
Publish one link used during incidents and after them. Engineers move from symptom to cause in the same view instead of switching between three tools and matching timestamps by hand.
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Why choose Emergent?
Most tools give you a demo you have to rebuild. Emergent gives you a product that is ready to run.
| Comparison | Other Tools |  |
|---|---|---|
| Built for | Demos and MVPs | Products you keep growing |
| What you get | Front end shell only | Full stack, wired end to end |
| Backend and database | Set it up yourself | Built and connected for you |
| Customization | Surface level styling | Deep workflow control |
| Integrations and APIs | Manual glue work | Connected from a prompt |
| Code ownership | Locked to the platform | Clean code you can export |
| Time to launch | Weeks of patching and setup | Live the same day |
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