Production incident root-cause coordination portal

For

Software engineering and SRE teams running production services

Solves

Production incidents span logs, metrics, traces and code, so teams lose time correlating signals, triaging duplicates and writing post-mortems by hand.

Delivers

Reviewer-approved root-cause findings and draft post-mortems

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Cut time-to-root-cause and incident coordination effort while keeping engineers in control of fixes.

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.
6. Detect AI-specific failures such as task failures and user frustration.
7. Track successful AI behaviors and custom user-defined issues.
8. Cluster user data and recurring errors into themes.
9. Highlight the exact step where an AI agent failed.
10. Query event data and traces in natural language.
11. Suggest code changes or pull requests for review.
12. Replay failing steps and measure changes after edits.
13. Flag pre-production performance and scaling risks.
14. Generate plain-English summaries and incident reports with timelines and commit histories.
15. Organize threaded notifications in chat and email.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned reviewer-approved root-cause finding and draft post-mortem with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Connected telemetry, alert, code and incident sources

2. 1

   Confirm the buyer's problem and scope

3. 2

   Connect permitted telemetry

4. 3

   Alert

5. 4

   Code and incident sources

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved root-cause findings and draft post-mortems

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, source connections, incident versions, reviewer comments, approval states, usage allowances, retention limits, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: detect production issues across infrastructure, application performance, logs and real user monitoring; merge related alerts into single incidents. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will software engineering and SRE teams running production services use it to solve "production incidents span logs, metrics, traces and code, so teams lose time correlating signals, triaging duplicates and writing post-mortems by hand"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Time to root cause, duplicate alert volume and accepted post-mortems per incident.
4. Measure, then decide. Track time to root cause and duplicate alert volume and accepted post-mortems per incident; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and incident volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved root-cause findings and draft post-mortems. Retain the explicit scope boundary: One connected production service and one observability stack; final root-cause confirmation and code changes remain engineering decisions.

What the build depends on. Telemetry ingestion and preview, asynchronous analysis jobs, editable incident history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One connected production service and one observability stack; final root-cause confirmation and code changes remain engineering decisions.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined service and incident set. Offer a monthly production allowance after repeat demand. Quote complex multi-service or regulated deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved root-cause finding and draft post-mortem. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Cut time-to-root-cause and incident coordination effort while keeping engineers in control of fixes. Demonstrate a concrete reviewer-approved root-cause finding and draft post-mortem using the buyer's approved incident and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software engineering and SRE professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved root-cause finding and draft post-mortem from a small authorized incident set, with a transparent calculation of time to root cause, duplicate alert volume and accepted post-mortems per incident and no promised savings.

The first 30 days

1. Week 1: interview five software engineering and SRE teams running production services and inspect a recent example of production incidents spanning logs, metrics, traces and code that lose time correlating signals, triaging duplicates and writing post-mortems by hand.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure time to root cause, duplicate alert volume and accepted post-mortems per incident, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Time to root cause, duplicate alert volume and accepted post-mortems per incident. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Time to root cause, duplicate alert volume and accepted post-mortems per incident; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved root-cause findings and draft post-mortems. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved incident patterns, service constraints and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative incident cases, reviewer corrections and verified operating constraints for software engineering and SRE teams running production services. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Small Hours, Struct, Microtica AI Incident Investigator, Ops AI by Middleware, Raindrop, Phare Incident AI, Okareo, Atla and Digma Preemptive Observability, plus manual dashboards and on-call runbooks. Compare this product with the buyer's present method on time to root cause, duplicate alert volume and accepted post-mortems per incident. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Telemetry ingestion, model calls, storage, reviewer hours, on-call coordination and licensed source data. Additional initial validation requires representative authorized incident preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved root-cause findings and draft post-mortems. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.