This is the story of how we got here — from late-night PDF wrestling to building something that actually makes reading smarter. Kiwizdom lets you highlight PDFs, annotate in Markdown, search semantically through your notes, and chat directly with your documents.
The Problem: When Note-Taking Tools Become the Enemy
Let's be honest — you're probably drowning in note-taking apps. Notion, Obsidian, Logseq, Roam, and whatever new "second brain" launched last week. Each one promises to revolutionize how you think, but here's what actually happens:
You spend hours crafting the perfect template. You nest pages within pages. You create elaborate tagging systems. You tweak your dashboard until it's Pinterest-worthy.
And then? You never use it. Not because you're lazy, but because you've built a system so complex that using it feels like work. Your beautiful organizational structure becomes a barrier between you and your thoughts.
What If Reading Could Be a Conversation?
We started asking different questions:
- What if highlighting wasn't just marking text, but starting a dialogue with your document?
- What if your notes could understand context, not just keywords?
- What if you could ask your PDF, "What were the main sustainability risks?" and get an actual answer?
- What if the tool stayed out of your way until you needed it?
Core question: How might we help users transform dense documents into searchable, actionable knowledge — with zero friction?
Listening to Real People (Not Just Assumptions)
Before writing a single line of code, we talked to the people who live in PDFs: graduate students, researchers, consultants, analysts. We asked simple questions: "Walk me through how you read a 50-page report." "What happens to your highlights afterward?" "What makes you want to throw your laptop out the window?"
Three insights
- "I highlight everything and remember nothing" — Everyone highlighted religiously. No one ever reviewed their highlights.
- "My knowledge is scattered across five different apps" — Quotes lived in PDFs. Thoughts went to Notion. Ideas stayed in heads.
- "Search is embarrassingly dumb" — People wanted semantic search: search by meaning, not exact words.
Meet Mia: Our North Star User
Mia, 23, Design Student & Startup Intern
Mia represents our core user perfectly. She's tech-savvy and loves tools like Notion and Obsidian — in theory. In practice, she gets overwhelmed by endless customization options and abandons her carefully crafted systems. She wants to think better, not organize better.
Our Solution: Intelligence Without Complexity
The Problem Statement: Students and knowledge workers using block-based tools often fall into over-customization traps. The cognitive overhead of managing complex systems outweighs their benefits, leaving users with beautiful dashboards they never touch.
Our Approach: We designed Kiwizdom around a simple principle: Progressive complexity. Give users just enough structure to connect ideas, but never enough to get in their way.
- Start with highlighting — the most natural reading behavior
- Add context when you want it — expand highlights into full notes
- Search by meaning, not keywords — semantic search that understands intent
- Chat with your documents — ask questions and get answers grounded in your actual annotations
- Stay focused on thinking, not organizing
Under the Hood: Building for Speed and Intelligence
We chose our tech stack with two goals: blazing fast performance and seamless AI integration.
Core Stack
- Frontend: Next.js + React + TypeScript
- Editor: Markdown-based blocks (Blocknote)
- Database: MongoDB
- AI: LangChain
- Auth: Clerk
The Magic is in the Details:
- Block-based architecture inspired by Notion, but with Markdown as the source of truth
- AI that augments your thinking instead of replacing it
- Contextual suggestions that appear when helpful, disappear when not
- Semantic search that actually understands what you're looking for
Why This Niche? The Strategic Bet on PDF-First Thinking
PDFs are where serious thinking happens — academic research papers, corporate reports, legal contracts, technical specs. These are dense, important reads. Most tools treat PDFs as an afterthought; we built Kiwizdom around them because high-stakes reading demands better tools and AI becomes particularly valuable here.
The Feature Philosophy: Intention Over Convention
Sometimes you need to ignore user requests and trust your instincts about what they actually need.
Key principles
- Start with the smallest possible interaction — highlighting is universal.
- Make intelligence feel magical, not mechanical — AI should understand context and role.
- Optimize for flow states, not feature completeness — minimal UI while reading deeply.
- Memory is more valuable than organization — semantic memory beats upfront categorization.
What Building This Taught Me About Product Strategy
Conventional PM wisdom says: build an MVP, get user feedback, iterate quickly. What actually worked for Kiwizdom was building for the job-to-be-done, saying no to surface-level feature asks, and shipping features users didn't know they needed until they experienced them.
Semantic search, chat that references annotations, and progressive note expansion were not first-line requests — they were discovered by observing real workflows.
What's Next?
We're not trying to replace your note-taking app. We're trying to make the reading and thinking part smarter. The roadmap focuses on making the AI more contextually aware — understanding not just what you highlight, but why you highlighted it and what you're trying to accomplish.
Ready to turn your next PDF from a chore into a conversation?
Want to follow along as we build? I'll be sharing more behind-the-scenes stories about unconventional product decisions, technical challenges, and the messy reality of building AI-powered tools. The journey from idea to product is full of surprises — and I'd love to have you along for the ride.