AI app for it and development · no coding needed
Production incident root-cause coordination portal
Cut time-to-root-cause and incident coordination effort while keeping engineers in control of fixes.
Made for: Software engineering and SRE teams running production services
What it does for you
The problem
Production incidents span logs, metrics, traces and code, so teams lose time correlating signals, triaging duplicates and writing post-mortems by hand.
What it gives you
Reviewer-approved root-cause findings and draft post-mortems
What you give it
Connected telemetryalertcodeincident sources
Build your own version of Small Hours, Struct and more
One app with what these 9 AI tools do, yours to keep and change: Small Hours, Struct, Microtica AI Incident Investigator, Ops AI by Middleware, Raindrop, Phare Incident AI, Okareo, Atla, Digma Preemptive Observability.
Everything these tools do, in one app
- Automated root cause analysis Automatically identifies the likely source of software issues to speed up troubleshooting.Found in Small Hours, Struct, Microtica AI Incident Investigator and 1 more
- AI-driven issue triaging Prioritizes and categorizes alerts to help teams focus on the most important issues.Found in Small Hours
- Cross-telemetry correlation Combines logs, metrics, traces, and code to find patterns and likely causes of incidents.Found in Struct
- Regression and anomaly correlation Surfaces spikes and patterns tied to alerts to identify anomalies.Found in Struct
- Auto-generated incident reports Creates incident summaries and post-mortems with charts, timelines, and commit histories.Found in Struct, Phare Incident AI
- Plain English summaries Provides clear, non-technical explanations of incidents to reduce confusion.Found in Microtica AI Incident Investigator
- Automatic issue detection Detects production issues across infrastructure, application performance, logs, and real user monitoring.Found in Ops AI by Middleware
- AI-generated fix suggestions Proposes code changes or pull requests to resolve identified problems.Found in Ops AI by Middleware, Atla, Digma Preemptive Observability
- Real-time alerts Notifies teams immediately when issues arise.Found in Ops AI by Middleware
- AI-specific issue detection Identifies problems unique to AI systems, such as task failures or user frustration.Found in Raindrop
- Win tracking Highlights successful AI behaviors to reinforce positive outcomes.Found in Raindrop
- Custom issue tracking Allows users to define and monitor specific issues or themes relevant to their product.Found in Raindrop
- Topic clustering and signals Clusters user data in real-time to reveal popular use cases and patterns.Found in Raindrop
- Deep research and traces Enables natural language searches across event data and traces every step of calls.Found in Raindrop
- Smart incident merging Groups related alerts into single incidents to reduce duplicate notifications.Found in Phare Incident AI
- Threaded notifications Organizes alert conversations in chat platforms and email.Found in Phare Incident AI
- Expanded incident timeline Provides richer timelines and metadata for faster triage.Found in Phare Incident AI
- Monitoring enhancements Adds features like request bodies for API tests, certificate tracking, and assertions.Found in Phare Incident AI
- Automated data processing Cleans and prepares datasets effortlessly for analysis.Found in Okareo
- Predictive analytics Uses customizable machine learning models to forecast trends and outcomes.Found in Okareo
- Interactive dashboards Visualizes data for easy insight sharing.Found in Okareo
- Natural language query interface Allows users to ask questions in plain language to get insights.Found in Okareo
- Step-level failure detection Highlights the exact step where an AI agent went wrong.Found in Atla
- Clustering of recurring errors Groups recurring error patterns to prioritize high-impact failures.Found in Atla
- Step annotations and trace querying Enables investigation of root causes without sifting through raw logs.Found in Atla
- Testing and replay tools Replays failing steps and measures changes after edits to validate fixes.Found in Atla
- Pre-production issue detection Identifies performance and scaling issues before they impact live applications.Found in Digma Preemptive Observability
- Scalability assessment Pinpoints which parts of the codebase will scale smoothly and which might create bottlenecks.Found in Digma Preemptive Observability
- IDE and code integration Provides insights directly within development environments.Found in Digma Preemptive Observability
- OpenTelemetry integration Supports OpenTelemetry for observability data across languages and platforms.Found in Small Hours, Digma Preemptive Observability
- Integration with observability platforms Connects with tools like Datadog, Sentry, and New Relic to fit existing workflows.Found in Small Hours, Struct
- Microservices monitoring Configures monitoring for microservices as individual services.Found in Small Hours
- User-friendly interface Provides a clear and intuitive web application to minimize learning curve.Found in Small Hours
- Compliance options Offers compliance such as SOC 2 Type II and HIPAA for regulated environments.Found in Struct
- Quick setup Enables fast deployment in minutes.Found in Struct
- Cloud infrastructure support Supports monitoring of components like ECS Fargate task health.Found in Microtica AI Incident Investigator
- Continuous learning AI agent continuously learns from system data to improve accuracy over time.Found in Microtica AI Incident Investigator
- Synthetic monitoring and browser testing Enhances reliability and user experience monitoring.Found in Ops AI by Middleware
- Integration with collaboration tools Connects with Slack, GitHub, Linear, and coding agents for handoffs.Found in Struct
How it works, step by step
- Detect production issues across infrastructure, application performance, logs and real user monitoring
- Merge related alerts into single incidents
- Triage and prioritize alerts by impact
- Correlate logs, metrics, traces and code
- Surface regression and anomaly spikes tied to alerts
- Detect AI-specific failures such as task failures and user frustration
- Track successful AI behaviors and custom user-defined issues
- Cluster user data and recurring errors into themes
- Highlight the exact step where an AI agent failed
- Query event data and traces in natural language
- Suggest code changes or pull requests for review
- Replay failing steps and measure changes after edits
- Flag pre-production performance and scaling risks
- Generate plain-English summaries and incident reports with timelines and commit histories
- Organize threaded notifications in chat and email
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewer-approved root-cause finding and draft post-mortem with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Production incident root-cause coordination portal with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Production incident root-cause coordination portal with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links7 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data197 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Cut time-to-root-cause and incident coordination effort while keeping engineers in control of fixes. For software engineering and SRE teams running production services, convert connected telemetry, alerts, code and incident history into reviewer-approved root-cause findings, merged incidents and draft post-mortems. The benefit is a testable hypothesis, measured through time to root cause, duplicate alert volume and accepted post-mortems per incident; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, connect permitted telemetry, alert, code and incident sources, then follow this sequence: 1. Detect production issues across infrastructure, application performance, logs and real user monitoring. 2. Merge related alerts into single incidents. 3. Triage and prioritize alerts by impact. 4. Correlate logs, metrics, traces and code. 5. Surface regression and anomaly spikes tied to alerts. Resolve uncertain cases with qualified reviewers, approve reviewer-approved root-cause findings and draft post-mortems, and measure time to root cause, duplicate alert volume and accepted post-mortems per incident against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One connected production service and one observability stack; final root-cause confirmation and code changes remain engineering decisions. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, log accuracy and access permissions. Engineers approve substantive changes and production actions. One connected production service and one observability stack; final root-cause confirmation and code changes remain engineering decisions. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One connected production service and one observability stack; final root-cause confirmation and code changes remain engineering decisions. Implement one approved input format, a bounded representative incident set and the first two task modules: detect production issues across infrastructure, application performance, logs and real user monitoring; merge related alerts into single incidents. Support the third module with operator review: triage and prioritize alerts by impact. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
OpenTelemetry, Datadog, Sentry, New Relic, Slack, GitHub, Linear, coding agents and cloud infrastructure such as ECS Fargate. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Incident queue, Investigation workspace, Review and post-mortem. Use a filterable incident list, a central timeline with correlated logs, metrics, traces and commits, and a right-hand panel for hypotheses, fix suggestions and comments. Let users compare merged alerts side by side. Display open, investigating, mitigated and closed states. Provide a shareable post-mortem link with comments anchored to the relevant signal. Make the task-specific outcome reviewer-approved root-cause findings and draft post-mortems visible beside its evidence, review state and value baseline.
