ScopeCast - NLP Feedback Intent Classifier That Turns User Emails Into Prioritized Backlog Items
Product teams drown in unstructured user feedback emails, Intercom threads, and App Store reviews — none of which maps cleanly to a backlog. ScopeCast classifies every piece of feedback by intent (bug, feature request, churn signal, praise), extracts the core ask, and pushes a structured ticket to Linear or Jira automatically. Finally, your inbox becomes a roadmap.
Difficulty
intermediate
Category
NLP & Text AI
Market Demand
High
Revenue Score
8/10
Platform
Web App
Vibe Code Friendly
No
Hackathon Score
🏆 7/10
What is it?
Every solo PM or indie founder manually triage 50+ feedback emails a week, losing hours that should go to building. ScopeCast connects to Gmail or Intercom, pulls incoming feedback, runs it through a fine-tuned intent classification pipeline (bug vs. feature vs. churn vs. praise), extracts the structured core request using Claude, and creates a prioritized Linear or Jira ticket with severity score and user context. The NLP classification layer uses a lightweight HuggingFace zero-shot classifier for fast pre-filtering, with Claude handling nuanced extraction — keeping API costs low. This is the feedback triage layer that Linear and Jira refused to build because it's outside their scope.
Why now?
HuggingFace's Inference API now runs zero-shot classification at under $0.001 per call, making per-feedback classification economically viable at indie scale for the first time in April 2026.
- ▸Zero-shot intent classification: HuggingFace classifies each feedback item as bug, feature, churn signal, or praise without training data.
- ▸Structured ticket creation: Claude extracts the core request and pushes a formatted ticket to Linear or Jira via OAuth.
- ▸Churn signal alerting: items classified as churn signals trigger an immediate Slack or email alert to the founder.
- ▸Priority scoring: combines recurrence count and churn signal weight into a numeric priority score per feature request.
Target Audience
Solo PMs and indie SaaS founders managing 50-500 user feedback items per month, estimated 80k+ on Linear's user base and r/ProductManagement.
Example Use Case
Priya, a solo PM at a 3-person SaaS, connects her Intercom inbox, and within 10 minutes ScopeCast has classified 200 backlog emails, flagged 12 churn signals, and pushed 40 feature requests to her Linear board with severity scores.
User Stories
- ▸As a solo PM, I want incoming feedback emails automatically classified by intent, so that I stop spending 3 hours a week on manual triage.
- ▸As a SaaS founder, I want churn signals flagged immediately, so that I can reach out to at-risk users before they cancel.
- ▸As a product team lead, I want classified feedback pushed directly to Linear as structured tickets, so that my backlog reflects real user needs without manual entry.
Acceptance Criteria
Classification: done when 85%+ of test feedback items are correctly classified across 4 intent categories. Linear Push: done when a classified feature request creates a formatted Linear ticket with title, description, and priority score. Churn Alert: done when a churn-classified item triggers a Slack message within 60 seconds of classification. Dashboard: done when all classified items display with intent badges and priority scores sorted correctly.
Is it worth building?
$39/month x 30 users = $1,170 MRR at month 2. $39/month x 130 users = $5,070 MRR at month 6. Math: 4% conversion on 3,000 trial signups via Linear community and r/ProductManagement.
Unit Economics
CAC: $15 via Linear community direct outreach. LTV: $351 (9 months at $39/month). Payback: 2 weeks. Gross margin: 85%.
Business Model
SaaS subscription
Monetization Path
Free tier: 50 feedback items/month. Paid $39/month: unlimited, Linear/Jira push, churn signal alerts, priority scoring.
Revenue Timeline
First dollar: week 3 via beta upgrade. $1k MRR: month 3. $5k MRR: month 7.
Estimated Monthly Cost
Claude API: $45, HuggingFace Inference API: $20, Vercel: $20, Supabase: $25, Stripe fees: $12. Total: ~$122/month at launch.
Profit Potential
Full-time viable at $6k–$12k MRR.
Scalability
High — can add Slack, Zendesk, and App Store review connectors, plus team dashboards.
Success Metrics
Week 1: 100 signups. Week 2: 15 paid. Month 2: 80% retention.
Launch & Validation Plan
Post a manual classification audit of 50 public App Store reviews for a known SaaS on Twitter, show the insights, link to waitlist.
Customer Acquisition Strategy
First customer: DM 15 Linear power users on Twitter offering free 3-month beta in exchange for weekly accuracy feedback calls. Ongoing: Linear and Jira app marketplaces, r/ProductManagement, ProductHunt launch, Twitter PM community.
What's the competition?
Competition Level
Medium
Similar Products
Productboard is enterprise-priced and requires manual tagging. Canny lacks intent classification and backlog push. Intercom has tagging but no structured ticket creation or churn signal detection.
Competitive Advantage
Linear and Jira have no native AI feedback ingestion. Productboard is $20k/year and enterprise-only. ScopeCast is the $39/month version for indie PMs.
Regulatory Risks
Low regulatory risk. User feedback data must not be used for model training without consent. GDPR deletion endpoint required.
What's the roadmap?
Feature Roadmap
V1 (launch): Gmail/Intercom ingestion, zero-shot classification, Linear push, churn alerts. V2 (month 2-3): Jira integration, custom classification labels, weekly digest email. V3 (month 4+): App Store review connector, team accounts, trend analytics dashboard.
Milestone Plan
Phase 1 (Week 1-2): classification pipeline + Supabase ships, 5 beta PMs test with real feedback. Phase 2 (Week 3-4): Linear push + Stripe live, first paid user converts. Phase 3 (Month 2): ProductHunt launch, 30 paid users target.
How do you build it?
Tech Stack
Next.js, Claude API, HuggingFace Inference API, Linear API, Jira API, Supabase, Stripe — build with Cursor for NLP pipeline, v0 for dashboard
Suggested Frameworks
HuggingFace Transformers (zero-shot classification), LangChain, Supabase JS
Time to Ship
3 weeks
Required Skills
HuggingFace zero-shot classification, Claude API prompt engineering, Linear and Jira OAuth, Next.js.
Resources
HuggingFace zero-shot docs, Linear API docs, Anthropic docs, LangChain quickstart.
MVP Scope
api/classify.ts (HuggingFace + Claude pipeline), api/linear-push.ts, api/intercom-webhook.ts, pages/dashboard.tsx, components/FeedbackCard.tsx, components/IntentBadge.tsx, lib/hf.ts, lib/claude.ts, lib/supabase.ts — built with Cursor.
Core User Journey
Connect Gmail or Intercom -> first batch classified in 5 minutes -> churn signal alert fires -> upgrade to push tickets to Linear.
Architecture Pattern
Intercom webhook fires -> classify.ts runs HuggingFace zero-shot -> Claude extracts structured request -> Supabase stores result -> Linear API creates ticket -> churn signal triggers Slack webhook.
Data Model
User has many FeedbackItems. FeedbackItem has one Classification. Classification has one ExtractedTicket. ExtractedTicket belongs to one LinearProject.
Integration Points
HuggingFace Inference API for zero-shot classification, Claude API for structured extraction, Linear API for ticket creation, Jira API for ticket creation, Stripe for payments, Supabase for storage, Slack Webhooks for churn alerts.
V1 Scope Boundaries
V1 excludes: App Store review connectors, team accounts, custom classification labels, mobile app, Zendesk integration.
Success Definition
A solo PM connects Intercom, receives a classified and prioritized Linear board from real feedback within 10 minutes, and renews after month one without founder contact.
Challenges
The hardest non-technical problem is convincing PMs that AI-classified tickets are trustworthy enough to act on without manual review — must show high accuracy on real feedback in the onboarding demo or users will never trust the automation.
Avoid These Pitfalls
Do not promise 99% classification accuracy — zero-shot on messy user emails caps around 85% and overpromising kills trust. Do not build Jira and Linear simultaneously in v1 — pick one and do it perfectly. Finding first 10 paying customers will take longer than the build — spend week 1 on outreach not code.
Security Requirements
Supabase Auth with Google OAuth, RLS on all user and feedback tables, 100 req/min rate limit, feedback content encrypted at rest, GDPR deletion endpoint and no training data use without consent.
Infrastructure Plan
Vercel for frontend and API, Supabase for Postgres and auth, GitHub Actions for CI, Sentry for error tracking, HuggingFace Inference API as external ML service — total infra under $130/month at launch.
Performance Targets
75 DAU and 600 req/day at launch, classification pipeline under 3 seconds per item, dashboard load under 2s LCP, HuggingFace results cached per identical text to reduce API calls.
Go-Live Checklist
- ☐Security audit complete
- ☐Payment flow tested end-to-end
- ☐Sentry live
- ☐Vercel Analytics configured
- ☐Custom domain with SSL
- ☐Privacy policy and terms published
- ☐5 beta PMs signed off on accuracy
- ☐Rollback plan documented
- ☐ProductHunt and r/ProductManagement launch posts drafted.
How to build it, step by step
1. Run npx create-next-app scopecast --typescript. 2. Install @supabase/supabase-js, @anthropic-ai/sdk, stripe, axios. 3. Set up Supabase tables: users, feedback_items, classifications, tickets. 4. Build HuggingFace zero-shot classification endpoint using facebook/bart-large-mnli. 5. Build Claude extraction prompt that converts classified text into a structured Linear ticket JSON. 6. Build Linear OAuth flow and ticket creation endpoint. 7. Build Intercom webhook receiver to auto-ingest feedback. 8. Build dashboard with FeedbackCard and IntentBadge components using v0. 9. Add Stripe checkout gating unlimited items and Linear push. 10. Deploy to Vercel and run end-to-end test with 50 real feedback samples.
Generated
April 11, 2026
Model
claude-sonnet-4-6