The customer feedback tool for feedback nobody sent you

Feedback customers volunteer in public — Reddit threads, X posts, community discussions, reviews — rather than answers to questions you asked. It's more candid than survey responses because it's written for peers, not for you, and it covers the silent majority who never answer an NPS prompt. The catch is that it's scattered across platforms and rarely tags your brand, which is the problem Octolens solves: collecting it, tagging it, and routing it like any other feedback channel.

Survey tools like Qualtrics or Typeform measure what customers say when you ask; Octolens captures what they say unprompted. The two are complementary — surveys give you quantified answers to your questions, public feedback gives you the questions you didn't know to ask. Teams typically run both: NPS for the metric, Octolens for the verbatims that explain it.

A feedback board collects requests from users motivated enough to find your board and post there — a small, engaged slice. Octolens captures the requests everyone else posts on Reddit, X, or GitHub instead. Many teams pipe Octolens mentions into their board or tracker via webhook, so public requests and board requests accumulate in one place.

Every collected mention is read by AI and labeled with topical tags — feature_request, bug_report, complaint, praise, question, and more — plus a sentiment label (Positive, Negative, Neutral) and a relevance score. Tags are filterable everywhere: feeds, Slack routing, webhooks, and the API, so "all bug reports this week" or "feature requests mentioning exports" is a query, not an afternoon of reading.

Yes. Webhooks fire on new mentions matching a feed's filters — so a mention tagged bug_report can create a Linear issue with the original thread linked, and many teams add an AI agent in the middle to classify severity, deduplicate against existing issues, and draft the ticket. See our guide on turning mentions into Linear issues for the full recipe.

Yes — public conversations are the largest unsolicited VoC source available. Filter mentions by tag and sentiment, export via API, and you have verbatims segmented by theme: what people praise, what they struggle with, and the exact words they use. Teams use it for messaging validation, roadmap prioritization, and pairing with survey data for the full picture.

Every channel has a bias — public posts skew toward engaged users and strong opinions. The difference is direction: surveys are biased by who bothers to answer you, public feedback by who bothers to post at all. Because the biases differ, combining both gets you closer to the truth than either alone. Public feedback also has one unique property: it comes with context — the workflow, the alternatives considered, the reactions of other users in the thread.

Reddit (posts and comments), X/Twitter, LinkedIn, Hacker News, GitHub, Stack Overflow, YouTube, TikTok, Bluesky, dev.to, Medium, Product Hunt, podcasts, newsletters, news sites, and review and comparison pages across the web. Feedback concentrates differently per audience — developer products live on Reddit, GitHub, and HN; B2B SaaS skews LinkedIn and reviews — so broad coverage matters more than any single platform.

Yes — AI relevance scoring reads every matching post in context and filters name collisions out. Prisma, Tally, and Render are all common words, and all three run feedback workflows on Octolens. You can mark anything the AI gets wrong and the filtering tightens from your feedback.

Yes — feeds are saved filters, and each feed can deliver to its own destination. Product gets feature requests for its area, support gets complaints and bug reports, marketing gets praise for social proof, leadership gets the daily digest. One data stream, routed by tag, keyword, and sentiment.

Yes — via webhooks, the REST API, or the MCP server, included on every plan. Production workflows include agents that classify incoming mentions, file tickets with threads linked, flag churn-risk complaints for CS, and draft replies for human review. Structured JSON with tags and sentiment means agents don't need to parse raw posts.

Volume and sentiment per keyword and tag: are bug reports rising after a release, is praise growing for the feature you bet on, which product line drives the complaints. The analytics run on the same tagged data as your alerts, and everything is exportable via API if you want it in your own warehouse or dashboards.

Mentions refresh hourly on the Pro plan and in real time on Scale, with alerts pushed as mentions are collected and tagged. Bug reports and frustrated threads benefit from speed; feature-request analysis works fine on a daily rhythm — most teams run a fast channel and a slow digest side by side.

Pro is $199/month (15,000 mentions, hourly refresh, Slack and email alerts, API, webhooks, and MCP included). Scale is $599/month with real-time refresh, 50,000 mentions, news monitoring, and AI summaries. Full data access on every plan, 7-day free trial, no credit card required.

Track your brand and product names, let the AI tune for a day, then create two feeds: bug reports and complaints to the channel your responders watch, and a daily all-feedback digest for the wider team. Add question phrases and competitor keywords once the habit forms. The first untagged feature request usually shows up within days.