TicketSidekick - The FSD of Triage. Automates the Incident Lifecycle

Hacker News

I'm excited to share TicketSidekick, an AI platform we've built to transform how engineering teams handle support tickets and incidents. The Problem We're Solving: Our founding team saw firsthand how valuable engineering resources were being consumed by repetitive triage tasks. Our research shows triage engineers spend 32.3% of their time on manual alert correlation, ticket categorization, and routing—tasks that can be fully automated. What TicketSidekick Does: - Complete Triage Automation: Uses ML to ingest, correlate, and filter alerts, eliminating false positives - Intelligent Incident Routing: Auto-categorizes and routes incidents to the right teams - AI-Powered Resolution: Automatically resolves common issues and generates responses for FAQs - Deep Analytics: Provides actionable insights on support operations and system health Early Results: - 45% reduction in average response time - 40% decrease in ticket resolution time We've designed TicketSidekick to integrate seamlessly with existing tools like Zendesk, ServiceNow, Jira, and various monitoring platforms. Our Vision: We're building what we call "the FSD of Triage" - starting with AI assistance for human agents, then gradually increasing automation capabilities based on your comfort level. Calculate Your Savings: See how much your organization could save with our ROI Calculator: https://www.ticketsidekick.com/calculator We're now accepting companies for our limited access program. Would love HN's feedback on our approach and the problems we're trying to solve.

Visit product View on Hacker NewsMar 25, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, tasks · Missing: mac, macos, cursor

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, lua · Missing: https docs, just released, open source

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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