This founder's product is used by 50,000 teams because she foresaw the AI wave and accurately timed its release#
Hello everyone, I am Bao Fu.
What kind of product can quickly acquire 50,000 team customers and achieve $100,000 in monthly revenue? Here’s the answer:
Company
Kraftful
Founder
Yana Welinder
Revenue
Over $100,000 per month
Field: Product management tools
Core Pain Point: Product teams need to spend a lot of time organizing user feedback from multiple channels (customer service systems, app stores, user interviews, etc.) and converting it into actionable plans.
Yana Welinder has personally experienced the pain points faced by product teams. She recognized early on the potential of AI to solve these problems, founded Kraftful, and launched the product at the peak of the AI wave. Today, 50,000 teams are using her product.
Here are Yana's successful experience shares 👇
Table of Contents#
- Solving Real Pain Points
- 50,000 Teams
- Modular Development
- Organic Growth
- Conservative Customers
- Iterating from Small Steps
- Future Plans
Solving Real Pain Points#
As the founder and CEO of Kraftful, I also serve as a senior venture partner at Pioneer Fund (YC alumni fund) and a researcher at the Stanford Internet and Society Center. In my spare time, I co-founded a nonprofit organization, VC-Backed Moms, which supports over 500 entrepreneurs.
The idea of founding Kraftful stemmed from my personal experience in handling user feedback during the product management process. Previously, I worked in product roles at several tech companies—from being the second product manager at a unicorn to managing product teams for millions of users.
The biggest challenge at that time was how to organize user insights from multiple channels and form actionable plans without spending a lot of time dealing with spreadsheets. So, I created Kraftful to simplify and optimize this process.
In the past, I often started businesses as side projects, but this time I almost immediately devoted myself fully to Kraftful. After identifying the problem to solve, I applied to YC and was accepted. At that time, as the product lead at IFTTT, I had to resign immediately to join YC.
50,000 Teams#
Kraftful is changing the way product teams handle user feedback. We automatically integrate feedback from channels like Intercom, Zendesk, app stores, G2, and video conferencing platforms, converting it into feature requirement documents and user stories that can be directly synced to Jira/Linear.
Using a SaaS tiered pricing model, free users converted to paying customers on the day we launched our MVP, with most upgrades coming from teams needing to handle larger volumes of data.
Currently, we serve over 50,000 product teams, including companies like Google, Microsoft, Netflix, and Canva. We have analyzed 25 million pieces of feedback, saving users over 250,000 hours of manual processing time.
Modular Development#
The initial version of the product was completed by two engineers in a few months, and the MVP could only analyze weekly feedback data from Zendesk and app stores. We prioritized ensuring the quality of core keywords over complex features to ensure quick output of clear insights.
After launch, we immediately crashed due to API call limits, but this validated market demand. We maintained a streamlined iteration process, focusing on improving user experience and retention with a tech stack that includes:
- Frontend: React + Blitz for rapid interaction
- Backend: Database and API architecture supporting continuous synchronization of user feedback
- AI modular design: Flexibly switching the latest LLM models (currently using o1 and 4o models) at various stages of the analysis process, achieving scalable deployment through OpenAI and Azure APIs.
Organic Growth#
First-Mover Advantage#
We were one of the earliest teams to position ourselves in the LLM space, accurately timing the release window of the ChatGPT wave, gaining thousands of users upon launching on Product Hunt.
Word-of-Mouth#
We refuse paid promotions and achieve organic growth through the following methods:
- The founder personally responds to user inquiries on weekends
- Demonstration videos guide users on how to use the product
- Users spontaneously include us in "Top 10 AI Tools" lists
- Users actively leave messages to recommend us when we are overlooked
Public Building#
Continuously sharing product progress and challenges, maintaining deep interaction with the product leader community.
Endorsement Effect#
Early endorsements from top investment institutions like YC enhanced our credibility.
Conservative Customers#
Despite having 50,000 pioneering teams using Kraftful, convincing traditional teams to change their workflows remains the biggest challenge. My approach is:
- On-site demonstration of the entire process from data integration to analysis to document generation
- Showcasing the improvement in work efficiency
- Common user feedback: "I thought it was just a regular LLM shell, but it really solves work pain points."
The current challenge is how to quickly reach more conservative users, which requires expanding existing user outreach methods.
Iterating from Small Steps#
The MVP does not need to be perfect; it just needs to effectively solve a specific problem. Recommendations:
- Focus on real user pain points
- Maintain frequent communication with users
- Rapidly iterate and optimize the product
Future Plans#
Product direction:
- Expand support for more product team workflows
- Utilize more powerful AI models to solve more pain points
Personal plans:
- Support more overlooked entrepreneurs through VC-Backed Moms
- Continue to focus on unresolved issues in traditional fields
Practical Resources for Independent Developers#
Core Experiences#
-
MVP Validation Strategy
- Build a minimum viable product with a 2-person team in 3 months
- Focus on a single feature: weekly feedback data analysis
- Validate real demand through API crash events
-
Growth Methodology
- Leverage the Product Hunt release window (AI concept explosion period)
- The founder personally participates in user support (immediate responses/demonstrations on weekends)
- Refuse paid promotions, focusing on user self-propagation
-
Technical Architecture
- Modular LLM integration design (supporting switching to the latest models at any time)
- React + Blitz frontend for rapid prototyping
- OpenAI/Azure dual API to ensure service stability
Cognitive Breakthrough Points#
- Early user education: Breaking the "LLM shell" bias through "feature demonstration comparison tables"
- Pricing strategy: Tiered by data volume (free users naturally convert to paid scenarios)
- Crisis management: User compensation plan within 72 hours after API crash
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