Pain Insights

LLM-powered analysis that clusters threads into product-ready insights with sentiment and momentum.

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Track

⚡ AI & Intelligence

Why

Pain Insights transforms raw forum activity into structured, actionable product intelligence using LLMs.

Inspiration

Pain Insights is an AI-powered analysis layer built on top of the Foru.ms API.

It ingests raw community data (threads, and posts), uses LLMs to detect recurring themes and sentiment, and groups them into actionable product pain points. Complete with severity, momentum, and supporting evidence.

Instead of reading dozens of threads, product teams can instantly see what’s broken, how often it’s happening, and why it matters.

What it does

Pain Insights analyses community discussion threads and surfaces grouped, prioritised pain points instead of isolated complaints.

For each pain point, it provides:

  • A clear summary of the underlying issue
  • How often it appears (mentions)
  • Sentiment and momentum over time
  • Evidence links back to the original threads
  • Optional documentation insights highlighting gaps or improvements

The result is a concise, decision-ready view of what users are struggling with most.

How I built it

  • Thread analysis: Each forum thread is summarised using an LLM to extract sentiment, a short TL;DR, and a stable issue key.
  • Deterministic grouping: Threads are grouped by AI-generated issue keys, allowing related complaints (e.g. session issues, billing confusion) to be combined reliably.
  • Severity scoring: Mentions, sentiment, and momentum are combined into a single severity score for prioritisation.
  • AI summarisation: Grouped issues are summarised into concise product-level insights.
  • Documentation insights: The system can optionally suggest missing or unclear documentation sections based on recurring confusion.
  • Frontend: A responsive Next.js interface surfaces insights in a clean, scannable dashboard.

Challenges I ran into

  • Over-clustering vs under-clustering: Pure embedding similarity was too noisy. We shifted to AI-generated issue keys for stable, predictable grouping.
  • LLM consistency: Guardrails and normalisation were needed to keep categories and outputs reliable.
  • Signal vs noise: Ensuring single mentions don’t outweigh recurring issues required careful scoring logic.

Accomplishments that I'm proud of

  • Successfully combining multiple user complaints into a single, meaningful pain point
  • Producing insights that are actionable for product and documentation teams
  • Maintaining determinism and explainability rather than “black box” clustering
  • Building something useful with very small data samples

What I learned

  • AI works best when paired with deterministic structure
  • Product teams care more about trends than individual complaints
  • Clustering is as much a UX problem as a technical one
  • Clear summaries beat raw data every time

What’s next for Pain Insights

  • Cross-source ingestion (support tickets, reviews, Slack exports)
  • Time-based trend comparison (week over week, release impact)
  • Exporting insights to Jira, Linear, or Notion
  • Role-based views for Product, Support, and Docs teams

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