Opportunities › User feedback organization for product teams › Proposed app
Larkspur Notes
Collect user feedback in one place, sort it by topic, and ask it questions with sources.
- Score
- 51
- confidence 38
- Evidence
- 4
- items on the page
- Rivals
- 0
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2011
- Mature market · AI estimate
ScreensConcept
Desktop app: Product managers triage and analyze feedback in dense dashboards and analytics views that require sustained desk work. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Inbox
Synced 2 min ago42 new
- Billing
About this appAI proposal
Larkspur Notes pulls user feedback from widgets, an API and GitHub issues into one inbox. Each item is sorted by what it says (bug, request, question) and by what it is about, such as billing or a specific endpoint. The result is a triage list that stays tidy without manual tagging.
Ask plain questions like 'What do users want from the export API?' and get an answer with links to the exact feedback behind every claim. You can check the source before you act on it.
It is built for product managers and teams at SaaS products, developer tools and open-source projects, including PMs who came from engineering and want answers, not another pile of tickets.
- Tagline
- Your users already told you. Now find it.
- Voice
- ClearCalmPrecise
- Why this name
- Larkspur is a plain flower name, easy to say and unrelated to feedback tools. 'Notes' says it keeps what users wrote.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
One inbox for every source
Connect an embeddable widget, a REST API and GitHub issues. New feedback lands in one list with its origin kept.
No member yet
Sorted by content and target
Each item gets a type and a target, such as billing, onboarding or a named endpoint. Targets are editable and learn from your fixes.
No member yet
Ask your feedback
Chat in plain language. Every answer cites the original feedback items so you can open and verify them.
No member yet
Triage queue
Work through new items by target, merge duplicates, and mark each as planned, answered or dismissed.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#2F3E5CDeep Larkspur · primary#F7F4EEPaper · surface#1D2433Ink · text#E0A526Marigold · accent#6E9A87Sage · data
Typography
Aa
Larkspur Notes
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Your users already told you. Now find it.
PricingAI proposal
The material gives no competitor prices. These tiers, based on volume and AI access as the source suggests, are a starting point to test.
Free
Free
- 100 feedback items a month
- 1 source
- Auto-sorting
- 1 seat
Team
$39 a month
- 2,000 items a month
- All sources
- Dashboard
- 5 seats
Insights
$99 a month
- 10,000 items a month
- Ask your feedback with citations
- Unlimited seats
- Public roadmap view
Would you pay Team · $39 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Teams publish a public roadmap or 'you asked, we shipped' page and notify people who gave feedback. Those pages and emails carry a small Larkspur Notes credit.
- Do they become customers?
- Readers are mostly end users, not PMs, so few will buy. Open-source maintainers and developer-tool founders who see the pages might. Test the click-through from public pages to sign-ups.
- First customers
- Product managers at seed to Series A developer-tool companies with 5 to 30 staff, who get feedback via GitHub issues and a widget.
- Where to reach them
- Hacker News: Show HN post with a live demo that sorts a public repository's issues and answers questions with citations.
- Open-source maintainers: Offer free sorting of a project's GitHub issues in exchange for feedback and a public roadmap page.
- Product manager communities: Join PM Slack and forum threads, then offer a free audit of their messy feedback backlog.
- First milestone
- Ten teams connect a real source, and five pay for Team or Insights within 60 days.
Why people already pay for this
- Public claimProduct managers switching from engineering find existing user insight tools have significant shortcomings in answering specific questions and staying organized
- Public claimThe solution organizes feedback by content category and target to create a structured triage flow
- Public claimThere is demand across multiple product categories including SaaS platforms, developer tools, and open-source projects
- Public claimThe product handles varying scales of feedback volume effectively
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · AI model accessHosted LLM and embeddings APISorting by content and target, and answering questions with citations, needs text classification and retrieval.
- This app · Vector searchpgvector in Supabase PostgresFinds related feedback for duplicates and for grounding cited answers without a separate service.
RisksAI inference
- AIWrong categories or answers that misstate feedback would erode trust. Citations must be checkable, and users need quick ways to correct targets.
- CommercialExisting user insight tools already gather feedback. The edge, organization and answerable questions, is only claimed in public posts and needs testing with real PMs.
- DataFeedback may hold personal data or confidential details. Storing it and sending it to a model provider needs clear consent, retention and deletion rules.
- TechnicalLearning a product's own targets, such as endpoint names, from little data may be unreliable for new customers at first.
- PlatformThe GitHub connector depends on API limits and app permissions that may change.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.