Introducing IncidentIQ – Turning Production Incidents into Organizational Intelligence!

We are excited to present IncidentIQ, our AI-powered incident investigation and organizational memory platform, developed for Hack Bengaluru 4.0 – Microsoft 2026, in association with Devnovate.

💡 The Problem: Why Do Engineering Teams Keep Solving the Same Problems?

In modern software environments, production failures are inevitable. Database connection timeouts, server overloads, memory exhaustion, API failures and unexpected deployment issues can bring critical services to a halt.

When these incidents occur, engineers often spend hours examining logs, searching documentation, comparing previous failures and trying different solutions.

But there is a bigger problem: organizations may have already encountered and resolved similar incidents.

Unfortunately, valuable troubleshooting knowledge is often scattered across historical records, technical documents and individual experiences.

What if an organization could remember every important failure, learn from every successful fix and use that knowledge to investigate future incidents?

That question inspired us to build IncidentIQ.

Our Solution: IncidentIQ

IncidentIQ aims to transform traditional incident management from reactive troubleshooting into intelligent, knowledge-driven investigation.

Instead of treating every production failure as an isolated event, our platform connects current incident information with historical experiences to help engineers make more informed investigation decisions.

What Makes IncidentIQ Special?

1. AI-Powered Incident Investigation

Engineers can submit production error descriptions, affected services, severity levels and relevant logs or stack traces. IncidentIQ generates a structured investigation report containing possible root causes, technical hypotheses and recommended next steps.

2. Historical Incident Comparison

The platform compares current symptoms with previously recorded incidents to identify similarities, historical root causes and potentially relevant solutions.

This helps engineers begin investigations with useful context instead of starting from scratch.

3. Hindsight Memory Engine – Our Core Innovation

One of the most important aspects of IncidentIQ is its Hindsight Memory Engine.

It focuses on preserving organizational knowledge by learning from both successful and unsuccessful troubleshooting approaches.

For example, consider recurring database connection timeout incidents:

• Increasing connection-pool capacity may resolve traffic-driven exhaustion.
• Adding missing database indexes may address long-running queries.
• Correcting connection leaks may require changes to application code.
• Repeated service restarts may provide only temporary relief.

IncidentIQ brings these lessons together so that engineers can understand not only what worked, but also why certain approaches failed.

4. Intelligent Investigation Workflow

IncidentIQ organizes its analysis into ordered investigation steps, helping engineers examine connection-pool metrics, database limits, traffic patterns, query performance, connection leaks, deployments and network connectivity.

5. Confidence and Limitations Analysis

We believe responsible AI is especially important in production environments.

IncidentIQ distinguishes between historical evidence and possible explanations, communicates confidence levels and highlights information that still requires verification.

AI recommendations are intended to support engineering judgment, not replace it. Production changes must always be reviewed and validated by engineers.

6. Ask IncidentIQ – Conversational Knowledge Discovery

Engineers can ask natural-language questions about previous incidents and their outcomes.

For example:

"What successful solutions have worked for database connection timeout incidents?"

The goal is to make historical organizational knowledge easier to discover and use.

🛠️ Technology Behind the Project

We developed IncidentIQ using a full-stack architecture:

• React.js: Interactive frontend and user experience.
• Vite: Frontend development and build tooling.
• Python: Backend application logic.
• FastAPI: API development and frontend-backend communication.
• AI-assisted analysis: Incident investigation and contextual recommendations.
• Hindsight Memory Engine: Historical incident knowledge and learning.

The application includes dedicated interfaces for Dashboard, New Incident Investigation, Incident History, Memory & Learning Insights and Runbooks.

A Realistic Incident Investigation Scenario

To demonstrate the platform, we explored a practical production scenario:

A payment service starts returning HTTP 500 errors during checkout, accompanied by a database connection acquisition timeout.

IncidentIQ identifies several possible root causes and compares the current symptoms with historical payment-service incidents.

It then produces:

• Possible technical root causes.
• Historical incident comparisons.
• Eight ordered investigation steps.
• Targeted solutions based on verified causes.
• Confidence levels and investigation limitations.
• Immediate actions for engineering teams.

This demonstration highlights how historical knowledge and structured AI-assisted analysis can work together to support incident response.

Our Vision: From Incident Management to Organizational Intelligence

We see IncidentIQ as more than an incident reporting application.

Our long-term vision is to develop an intelligent operational knowledge platform that continuously improves how engineering teams investigate, understand and learn from system failures.

Some of the future possibilities include:

🔹 Integration with real-time monitoring, logs and distributed tracing.

🔹 Advanced semantic search for more accurate historical incident matching.

🔹 Automated correlation of incidents across multiple services.

🔹 Intelligent runbook recommendations.

🔹 Engineer feedback mechanisms to improve organizational memory.

🔹 Scalable deployment for enterprise engineering teams.

Why We Built This

Every production failure carries valuable information.

A successful fix represents experience. A failed approach represents a lesson. A recurring incident represents an opportunity to improve.

We believe this knowledge should never disappear when an incident is closed or when an engineer moves to another team.

With IncidentIQ, we want to make organizational experience accessible, reusable and actionable.

We are excited to share our work as part of Hack Bengaluru 4.0 – Microsoft 2026, and we look forward to learning, collaborating and improving through the Devnovate community.

🔗 Explore our project and source code on GitHub:

https://github.com/chiranth-art/OnCall-Memory.git

We would love to hear your thoughts!

What do you think is the biggest challenge in modern incident management: identifying root causes, finding previous solutions, or preserving engineering knowledge?

Let's build systems that don't just recover from failures, but learn from them.

IncidentIQ – Investigate Smarter. Remember Better. Build More Resilient Systems.

• With this take a closer look at IncidentIQ in action – Explore our intelligent dashboard, AI-powered investigation reports, and Hindsight Memory Engine. **

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