Alert investigation and fix console

For

Engineering and on-call teams running production software services

Solves

Alerts arrive faster than engineers can investigate, so root causes and fixes wait on scarce senior time.

Delivers

Source-linked root-cause findings and reviewable fix proposals

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce time from alert to reviewed fix while keeping engineers in control.

1. Receive alerts from connected alerting platforms.
2. Triage each alert and open an investigation automatically.
3. Pull context from telemetry, repository, docs and read-only databases.
4. Test hypotheses and rank candidate root causes with evidence.
5. Answer plain-language questions about system status and configuration.
6. Execute approved runbook steps to resolve known incidents.
7. Reply in the Slack thread where the alert appeared.
8. Suppress issues that code and telemetry do not show as real impact.
9. Open a mergeable pull request with a proposed fix for confirmed issues.
10. Compare the reviewed result with the recorded baseline and value assumptions.
11. Capture corrections and named-owner approval before consequential use.
12. Export a versioned source-linked root-cause findings and reviewable fix proposals with source references and unresolved questions.

Everything these tools do, in one app

• Automated alert triage Automatically investigates alerts to identify root causes before engineers engage.Found in Parity (YC S24), Superlog Responder
• Root cause analysis Determines the underlying cause of an issue and provides evidence.Found in Parity (YC S24), Superlog Responder
• Integration with alerting tools Connects to existing alerting platforms to receive and process alerts.Found in Parity (YC S24), Superlog Responder
• Automated runbook execution Executes predefined troubleshooting steps automatically to resolve incidents.Found in Parity (YC S24)
• Natural language interface Allows users to query system status and configurations using plain language.Found in Parity (YC S24)
• Proactive investigation Initiates investigations triggered by alerts or user prompts to gather data and test hypotheses.Found in Parity (YC S24)
• Symptom checker Analyzes user input to suggest possible medical conditions.Found in Doctor Droid
• Medical information database Provides access to a broad database of medical information and common illnesses.Found in Doctor Droid
• Personalized health tips Offers health tips based on symptoms and user profile.Found in Doctor Droid
• 24/7 availability Provides instant guidance at any time.Found in Doctor Droid
• Simple interface Offers a user-friendly interface optimized for mobile and desktop use.Found in Doctor Droid
• Slack-native incident response Lives in the same Slack channel where alerts appear and replies in the thread with analysis and evidence.Found in Superlog Responder
• Mergeable PR generation Opens a pull request with a proposed fix for confirmed issues, ready for human review.Found in Superlog Responder
• Full customization Allows editing of prompts, memory, repo access, and escalation rules to adapt to team standards.Found in Superlog Responder
• Open-source deployment Provides a free, self-hostable codebase with an optional cloud deployment.Found in Superlog Responder
• Noise reduction Only raises issues when code and telemetry indicate real impact, reducing false positives.Found in Superlog Responder
• One-click Slack sync Syncs with your Slack channel in one click without new telemetry installation.Found in Superlog Responder
• Context pulling Pulls context from connected data sources like Datadog, Sentry, Notion, repository, or read-only database.Found in Superlog Responder

What goes in, what comes out

The workflow

1. In
   Start with

   Connected alert streams, telemetry, repository context and runbooks

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected alert streams

4. 3

   Telemetry

5. 4

   Repository context and runbooks

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked root-cause findings and reviewable fix proposals

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the 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 service and one alert source; production changes and incident decisions remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Connected sources and rules, Investigation console, Fix review and delivery. Use a queue of open alerts, a large central investigation view, and a right-hand panel for evidence, timeline and comments. Let users compare candidate causes side by side. Display investigating, needs review and resolved states. Provide a Slack thread view with analysis anchored to the relevant alert. Make the task-specific outcome source-linked root-cause findings and reviewable fix proposals visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source connections, runbook versions, Slack channel mappings, approval states, usage allowances, investigation limits, download 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

Customer-owned alerting platforms, telemetry, repositories, documentation and read-only databases. Slack, cloud log storage, source control and ticketing destinations. 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: receive alerts from connected alerting platforms; triage each alert and open an investigation automatically. 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 engineering and on-call teams running production software services use it to solve "alerts arrive faster than engineers can investigate, so root causes and fixes wait on scarce senior time"?
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: Alert-to-root-cause time and accepted fix proposals per on-call hour.
4. Measure, then decide. Track alert-to-root-cause time and accepted fix proposals per on-call hour; 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 service and one alert source; production changes and incident decisions remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: receive alerts from connected alerting platforms; triage each alert and open an investigation automatically. Support the remaining modules with operator review: pull context from telemetry, repository, docs and read-only databases; test hypotheses and rank candidate root causes with evidence; answer plain-language questions; execute approved runbook steps; reply in the Slack thread; suppress issues without real impact; open a mergeable pull request. 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 case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around source-linked root-cause findings and reviewable fix proposals. Retain the explicit scope boundary: One connected service and one alert source; production changes and incident decisions remain engineering.

What the build depends on. Alert ingestion, telemetry access, repository read access, asynchronous investigation jobs, editable version 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 service and one alert source; production changes and incident decisions remain engineering.

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 alert source. Offer a monthly production allowance after repeat demand. Quote complex multi-service or regulated environments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked root-cause findings and reviewable fix proposals. 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

Reduce time from alert to reviewed fix while keeping engineers in control. Demonstrate a concrete source-linked root-cause findings and reviewable fix proposals using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering and on-call teams running production software services professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked root-cause findings and reviewable fix proposals from a small authorized input set, with a transparent calculation of alert-to-root-cause time and accepted fix proposals per on-call hour and no promised savings.

The first 30 days

1. Week 1: interview five engineering and on-call teams running production software services and inspect a recent example of alerts arrive faster than engineers can investigate, so root causes and fixes wait on scarce senior time.
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 alert-to-root-cause time and accepted fix proposals per on-call hour, 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: Alert-to-root-cause time and accepted fix proposals per on-call hour. 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

Alert-to-root-cause time and accepted fix proposals per on-call hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked root-cause findings and reviewable fix proposals. 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 runbooks, service constraints and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering and on-call teams running production software services. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Parity (YC S24), Doctor Droid, Superlog Responder, and the buyer's present mix of alerting tools, manual triage and internal scripts. Compare this product with the buyer's present method on alert-to-root-cause time and accepted fix proposals per on-call hour. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, telemetry and log processing, storage, reviewer hours, on-call shadowing and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked root-cause findings and reviewable fix proposals. 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 scope. One connected service and one alert source; production changes and incident decisions remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.