Feature Request Triage Workflow

You publish a feature. 3 users tweet about it, 1 complains on Reddit, 2 email support. The AI aggregates all six into one priority row on your roadmap.

Updated 2026-04-18
Tycoon solution

The AI Customer Support and AI CTO run a unified triage loop: ingest requests from every channel into one queue, deduplicate across channels (the 'dark mode' ask on Twitter and in Intercom is one row), score by paying-user weight + frequency + strategic fit, and route to the Linear roadmap with customer-facing updates when shipped. One row per idea, every voter traceable.

How it runs

  1. Multi-channel ingestion

    Tycoon subscribes to Intercom, Discord, Twitter mentions, Reddit (your brand + competitor subs), customer call transcripts (via meeting-notes workflow), Slack sales channel, Canny/Productboard widget, and the cold inbox. Every message is classified: bug, feature request, question, complaint, praise. Feature requests flow to the triage queue.

  2. Extract the underlying ask

    Users never describe features cleanly. 'This is so slow' might mean import speed, dashboard load time, or search latency. AI Customer Support rewrites each raw request into a crisp feature statement: 'Faster CSV import for files > 10K rows.' Writes who asked, when, their MRR, and the raw quote as context.

  3. Dedupe across channels and phrasings

    Three users say 'dark mode', one says 'night theme', one says 'please fix this white screen at 2am.' Same row. The AI clusters by semantic similarity and merges; each request carries its voters (name + MRR + channel + quote). You see 1 row with 5 voters, not 5 separate asks.

  4. Score by revenue and strategic fit

    Each row gets a score: sum(voter MRR) × frequency multiplier × strategic-fit weight (AI CTO inputs). A $3K/mo customer asking once outweighs three free users asking twice. Strategic fit comes from your stated priorities — 'we're focusing on enterprise this quarter' shifts weights toward enterprise-tagged requests.

  5. Weekly roadmap review with you

    Monday morning the AI posts the top 10 ranked requests in chat: title, score, voter list, estimated effort (AI CTO rough sizing). You approve, reject, or reshuffle. Approved items become Linear issues with the customer voter list in the description. Rejected items get a reason logged.

  6. Close the loop with voters

    When a requested feature ships, the AI Customer Support auto-messages every voter across whatever channel they originally asked on: a DM for Twitter, an Intercom reply for ticket filers, a Discord ping, etc. Each message is personalized — 'You asked about this on Feb 12th, here it is.' Not a broadcast; an individual receipt.

  7. Monthly theme synthesis

    First Monday of the month, AI CTO writes a 1-page synthesis: top themes across all requests (not just top requests), which customer segments are asking for what, which asks cluster suggest a bigger product direction, which asks keep coming back despite being rejected. Input to your next planning cycle.

Who runs it

  • hire/ai-customer-support
  • hire/ai-cto
  • hire/ai-head-of-growth

What you get

  • Every feature request from every channel captured in one queue
  • Requests deduplicated — no double-counting the loud asks
  • Prioritization weighted by actual revenue, not volume
  • Voters receive personal notifications when their ask ships
  • Roadmap decisions backed by voter lists you can show investors
  • Patterns that only emerge across channels surface in monthly synthesis
  • Support time freed from 'yes I've heard the request, we're thinking about it' replies
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

Won't the AI misclassify requests — turn bug reports into feature asks or miss the real problem behind a vague complaint?

Classification isn't 100%, which is why the AI Customer Support attaches the raw message to every triaged row and flags low-confidence calls for your review. In practice, ~85% of messages classify correctly on the first pass (bug vs. feature vs. question vs. praise). The other 15% land in a 'needs-review' bucket that you or your AI COO resolves in a few minutes daily. The bigger failure mode isn't misclassification — it's missing context. A user complaint that sounds like a feature request might actually be a pricing objection. The AI reads the full conversation history (Intercom thread, prior tweets, past calls) before classifying, which cuts the context-blind errors dramatically. You can also retrain its classifier on your product vocabulary in a few sessions.

My power users will notice if the AI replies to their tweet acknowledging a feature request. How do you handle AI-sounding outreach?

The AI doesn't post public replies to tweets — too easy to go wrong, too public when it does. It DMs instead, or leaves a like/ack on the public tweet and follows up privately. Tone is calibrated to your past DMs (the AI learns your voice from your prior founder replies) and messages are short: 'Saw your ask about X — logged it as issue #142, you're voter 3 of 5. Will update when it ships.' Direct, specific, traceable. The anti-pattern is generic 'thanks for the feedback, we'll consider it!' which everyone reads as AI immediately. Specificity is the tell of real engagement, and the AI leans hard into it.

How does this handle requests from prospects vs. paying customers? A prospect's ask and a paying customer's ask aren't equal.

They're weighted differently in the score. A paying customer's ask gets their MRR as the base weight. A prospect's ask gets weighted by the AI Head of Growth's deal-stage confidence: a prospect in active trial with a decision date next week gets near-customer weight; a cold inbound asking 'would you ever build X' gets a fraction. The scoring formula is visible — you can see why row A ranks above row B — and you can override the formula weights for your business. Some founders care more about expansion (weight existing customers heavily), others care more about new-segment expansion (weight prospects higher). The default balances both, you tune from there.

What if I reject a request and the customer complains publicly? Does the AI route that to me or handle it?

Anything that's trending publicly — a request denial going viral on Twitter, a Reddit thread picking up steam, a community revolt in Discord — escalates to you within 10 minutes of trend detection. The AI Head of Growth and AI Customer Support don't try to handle reputation events alone. What they do prepare: a timeline of the request, full context of why it was rejected, who the complainers are, similar cases from the past, and two or three suggested response paths (ship it anyway, double down on the rejection with clearer reasoning, offer a different solution). You decide and respond; the AI drafts in your voice and sends once you approve.

Won't consolidating voices from free users, paying customers, and prospects just make my roadmap bland — chasing the average?

It would, if you let the score be the decider. But the AI flags high-variance requests explicitly — 'this feature has 1 enterprise voter willing to commit $40K ARR vs 23 free-tier voters who'd use it casually' — so you can make strategic bets instead of popularity plays. The feature request system is meant to surface signal that would otherwise stay scattered, not to auto-rule the roadmap. Founders who use it well read the weekly digest and then make a decision; founders who use it badly let the score rank everything. The AI CTO's monthly synthesis deliberately highlights contrarian requests and asks from specific customer types to counteract the average-chasing tendency.

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