Know what's happening — before your users do.
Monitoring Center is CreateLixir's operational intelligence hub — metrics, traces, logs, alerts, and AI diagnostics unified inside the same workspace you build in. When production drifts, you find out first, and you have everything you need to fix it in one place.
Live dashboards · AI anomaly detection · Deployment-aware alerts · Incident timelines with project context
Not another metrics tab.
The operational intelligence layer.
Monitoring Center is the operational hub where developers gain real-time visibility into their applications — signals, incidents, and AI-assisted diagnostics unified into one intelligent experience.
Monitoring Center combines monitoring, observability, alerts, and diagnostics with the same project context the rest of the platform reads from. Instead of a dashboard that shows numbers and asks you to figure out what they mean, you get a workspace that already knows your architecture, your recent releases, and the code that runs behind every metric.
Regressions caught in production feed back into Testing Center as new tests. Performance patterns turn into architecture proposals in AI Architect. Every runtime signal is available to the rest of CreateLixir — so what production teaches you shapes what you build next.
The cost of not knowing
is always higher than you think.
Continuous monitoring isn't about dashboards — it's about detecting, understanding, and resolving problems fast enough that they never become the thing your team is known for.
Unexpected outages
The incident you learn about from a customer email is the incident that already cost you trust. Continuous monitoring inverts the order.
Slow performance
Slowness compounds silently across releases. Latency regressions caught early stay small; caught late, they redesign your product for you.
Errors affecting users
The stack trace that never reaches a developer becomes a user's frustration. Real-time error signals close that loop.
Infrastructure issues
Databases, queues, third parties — each fails in its own way. Unified observability means one place to see it happen.
Difficult troubleshooting
Correlating logs, metrics, and traces across five tools is where most on-call time goes. AI-assisted correlation returns those hours.
Limited visibility
"We don't have data for that" is the single most expensive sentence in operations. Instrumentation should be a default, not a project.
Growing complexity
The system gets more surfaces. The team gets more responsibilities. Without an operational hub, the load compounds without a plan.
Every layer,
every signal.
Monitoring Center is designed for the surfaces real applications actually have — application-level, infrastructure-level, AI-level, and end-user experience — under one roof.
Application health
Composite signals that tell you if the app is fine — before you look at any single metric.
API performance
Latency percentiles, error rates, and throughput per endpoint.
Response times
End-to-end and per-hop. Correlated with the deploy that caused any regression.
Error rates
Ranked by user impact, grouped by cause, ready to open in Code Studio.
AI services
Model latency, token usage, evals-passing rate, and prompt regressions — first-class signals.
Database health
Query performance, connection pressure, slow-query surface, and migration effects.
Background jobs
Queue depth, throughput, failure rate, and retry patterns.
Integrations
Third-party contracts and webhook flows watched end-to-end.
Infrastructure
CPU, memory, disk, network — surfaced next to the code that consumes them.
User experience
Real-user monitoring for interaction latency, layout shift, and error surfaces.
An intelligent
operations assistant.
AI in Monitoring Center reads your telemetry against project context — every anomaly, explanation, and recommendation reflects what your software actually is.
Detect unusual behaviour
Baseline-aware anomaly detection — request rates, latency shifts, error clusters — before the alerts you configured would have fired.
Identify bottlenecks
The slowest endpoint. The most-blamed dependency. Ranked by user impact, not raw magnitude.
Explain error patterns
A plain-language read of an error cluster — what it likely means, what it correlates with, where to look next.
Highlight trends
Slow drifts that don't set off alerts but shape long-term reliability — surfaced before they become emergencies.
Recommend improvements
Not just what's wrong — a concrete proposal for how to fix it, ready to open in Code Studio or AI Architect.
Predict issues
Signals that historically preceded incidents — flagged early, with an explanation you can share with the team.
Summarise incidents
A one-paragraph incident summary written from the timeline, ready to publish to your status page.
Suggest next steps
When an on-call is unsure, the AI proposes the next investigation — a chart to open, a log to search, a service to check.
A single, unified view
of everything running.
Dashboards designed to answer the questions on-call actually asks — not a menu of every metric a system can produce.
Live metrics
Real-time signals across every service, updated as they happen.
Performance charts
Latency, throughput, and error-rate charts with per-endpoint drilldowns.
Service health
Composite health for each service — one glance, one answer.
Deployment tracking
Every release annotated on every chart. See the effect of a deploy the instant it lands.
Resource usage
CPU, memory, disk, network — tied back to the code path that spends them.
Request analytics
Traffic patterns, top routes, geo distribution — the shape of who's using what.
Error timelines
When it started, when it peaked, what shipped around it.
Activity feeds
A chronological read of everything that changed in the last hour — humans and machines.
Fewer pages.
More signal.
Alerts are a first-class primitive — designed to reduce noise, respect on-call, and prioritise the incidents that actually matter.
Critical alerts
The signals that mean something is on fire, delivered without noise.
Performance warnings
Regressions that haven't crossed 'alert' yet but need eyes today.
Service degradation
Partial failures — the ones traditional binary alerts miss.
Deployment notifications
Every release annotated in your alert stream so cause-and-effect stays legible.
Error spikes
Threshold-based, seasonality-aware, and grouped by cause — not by count.
Custom rules
Compose your own conditions with the same primitives the AI uses to detect anomalies.
Future delivery channels — email, Slack, webhooks, and mobile push — light up progressively without changing how alerts are authored.
Cut minutes off
every incident.
Fast diagnosis is the difference between a minor blip and a customer email. Monitoring Center is designed for the moment things go wrong.
Error logs
Structured, searchable, and linked back to the exact commit + tests + telemetry.
Event timelines
Every meaningful signal around an incident on one timeline — no cross-tool detective work.
Deployment history
Which release was live when things broke. Rollback and re-deploy actions inline.
Performance metrics
Latency, error rate, and resource signals during the window, side-by-side.
AI-generated summaries
A one-paragraph incident summary written from the timeline, ready to publish or hand off.
Related activity
Adjacent services, other releases, upstream telemetry — everything you'd otherwise open five tabs for.
Every signal, everywhere,
in one workspace.
Monitoring Center reads from and hands off to every other product in CreateLixir. Production behaviour becomes fuel for the next iteration.
Project Brain
The memory the AI reads from when explaining a signal, anomaly, or incident.
AI Architect
Production patterns become concrete architecture proposals for the next iteration.
Code Studio
Every error links back to the code that caused it, ready to open and fix.
Testing Center
Regressions caught in production become tests that prevent recurrence.
Deployment Center
Post-release health is watched automatically. Rollback recommendations surface on the release view.
Insights
Aggregate signals across projects for the leadership view of reliability and performance.
API Center
Contract regressions and endpoint-level health tie back to the API definitions they belong to.
- Build
- Test
- Deploy
- Monitor
- Learn
- Improve
Monitoring is not the end of the loop. It's where the next iteration begins.
The difference,
by uptime and by hours saved.
Practical benefits — fewer incidents, shorter ones, better decisions, and a workflow that keeps the team out of dashboards and in the fix.
Complete visibility
One workspace for every signal your application produces.
Faster issue detection
Anomalies flagged before the alert you'd have configured would have fired.
AI-assisted diagnostics
Plain-language explanations grounded in the project — not generic advice.
Better performance
Bottlenecks ranked by user impact, ready to open in Code Studio or AI Architect.
Reduced downtime
Faster detection + faster diagnosis = shorter incidents, fewer regressions.
Faster incident response
Everything on-call needs on one timeline. Minutes off the average incident.
Smarter operational decisions
Trends surfaced as investable signals, not dashboards nobody reads.
Continuous improvement
Production teaches the workspace what to build, test, and deploy next.
Where Monitoring Center
is heading next.
Directions the platform is building toward. Not shipping today — the trajectory that shapes every decision inside Monitoring Center.
Predictive monitoring
The bugs likely to bite tomorrow, flagged today — from patterns in production behaviour now.
FutureAutonomous incident detection
Incidents opened, scoped, and paged automatically when telemetry crosses risk thresholds.
FutureAI root cause analysis
Cause-and-effect surfaced across services, not by hand across five dashboards.
FutureIntelligent scaling
Scaling recommendations grounded in real usage patterns, not static rules.
FutureSelf-healing workflows
Common remediations executed automatically, with a full audit trail.
FutureCross-project observability
Signals aggregated across projects for teams that run more than one product.
FutureBusiness metrics integration
Revenue, conversion, and engagement charts alongside latency and error rate.
FutureAutomated operational insights
Weekly operations summaries — what mattered, what shifted, what to change.
Future
The questions ops teams ask
before they change monitoring tools.
Adopting a new observability platform is a serious decision. These are the questions we hear most.
- Monitoring Center is CreateLixir's operational intelligence hub — a unified workspace for observing, understanding, and improving applications after they ship. It combines metrics, traces, logs, alerts, and AI-assisted diagnostics into one experience that reads the same project context as the rest of the platform.
Monitoring Center works with every part of CreateLixir.
Monitoring Center is the operational surface. These are the products it reads from, cooperates with, and hands off to.
Code Studio
Every error links back to the exact line, ready to open and fix.
Testing Center
Production regressions become tests that prevent recurrence.
Deployment Center
Every release is watched automatically; rollbacks surface on the release view.
Insights
Aggregate signals across projects for the leadership view.
Project Brain
The memory the AI reads from when explaining signals and incidents.
AI Architect
Production patterns become concrete architecture proposals for the next iteration.
Great software isn't just built.
It's continuously observed, improved, and refined.
Detect issues faster, understand incidents clearer, and let what your users experience in production shape what your team builds next.