DSTOR: A Case Study

---

Bringing Advanced Search to Web3: The DSTOR Story

In 2023, I found myself grappling with a frustrating problem—searching for a file I had uploaded to the Arweave blockchain. The Web3 space was exploding, yet despite its technological promises, a fundamental feature was missing: robust search and content discovery. Blockchain applications were generating massive amounts of user-generated content, but there was no effective way for users to find what they needed or intentionally discover new content they didn't even know about yet. This gap in functionality led to decreased engagement and retention, a problem especially visible in platforms like Lens Protocol, where users struggled to navigate the flood of new posts.

DSTOR was born out of this frustration. Initially, I built a simple tool for myself to solve the issue, but after conversations with developers and founders in the Web3 space, I discovered a broader demand. Many wanted to implement search within their applications but lacked the resources or technical expertise/staff to do so. If I could offer a scalable, plug-and-play solution, it had the potential to be a game-changer for Web3 apps.

“How powerful would it be,” we discussed, “if anyone building a Web3 app could instantly add advanced search and recommendations—without the headache of managing the infrastructure?”

Research and Validation

To validate the need for DSTOR, I spoke with over two dozen developers and app founders that I connected with through Discord, Twitter and Github. The consensus was clear: they wanted search, but the technical overhead of building and maintaining the infrastructure was a major barrier. I realized that a SaaS model could lower this barrier—allowing developers to integrate advanced search without the burden of infrastructure management.

The next step was building a proof of concept. I started with a minimal viable product (MVP)—a lightweight Python script using SQLite to index and search content from a single Web3 application using Faiss. I shared this MVP online, and interest grew rapidly, culminating in an invitation to join Longview Labs’ startup incubator. This gave DSTOR access to mentorship, access to new funding opportunities, and a community of early adopters to refine the product.

What makes search in Web3 so difficult?

Understanding the problem required a deeper dive. I spoke with developers and founders working across different Web3 applications, each struggling with unique but interconnected issues. Some platforms were overwhelmed with content, making discovery nearly impossible. Others faced technical limitations—blockchain data was fragmented, difficult to index, and expensive to process in real-time.

Synthesizing the most important pain points revealed several common themes:

illustration of a developer talking

“We want to add search, but maintaining a real-time index of blockchain data is too complex.”

illustration of a product manager talking

“Users are frustrated. They can’t find past content, so they stop engaging.”

illustration of a founder talking

“We don’t have the resources to build and maintain custom search infrastructure.”

illustration of a user talking

“I’d use these apps more if I could easily find what I’m looking for.”

These insights guided the development of DSTOR, ensuring the solution directly addressed the biggest obstacles developers and users faced.

Which formed some key insights:

🔍 Developers struggle with maintaining real-time blockchain search due to data fragmentation.

📉 Low engagement happens when users can’t easily find past content, leading to churn.

🛠️ Resource constraints prevent many teams from building and maintaining custom search solutions.

🔗 Users want discoverability—better search would drive deeper interaction with Web3 apps.

We observed distinct challenges between small-scale developers and larger Web3 platforms. Personas help us keep these different needs in mind moving forward:

---

Alex

🗣️ “I just want my users to find what they’re looking for—quickly.”

Alex is an independent Web3 developer working on a niche blogging platform. He built the app solo and is passionate about decentralization but struggles with user engagement. Without proper search, new users can’t find older content, leading to frustration and churn.

Goals:

✅ Improve content discoverability to keep users engaged.

✅ Implement search easily without managing complex infrastructure.

✅ Focus on building features, not maintaining backend search systems.

Frustrations:

❌ Users leave because they can’t find relevant posts.

❌ Existing search solutions are too expensive or require deep technical expertise.

❌ Blockchain data is fragmented, making indexing difficult.

---

Jordan

🗣️ “Our users expect a seamless experience—we need search to feel intuitive.”

Jordan leads product at a mid-sized Web3 social platform. Their team has scaled rapidly, but as content volume grows, engagement is dropping. Users complain about content being buried, and developers don’t have the resources to build an in-house search solution.

Goals:

✅ Keep engagement high by making content easy to discover.

✅ Provide an intuitive, scalable search feature without reinventing the wheel.

✅ Free up engineering time for core platform innovations.

Frustrations:

❌ Users get lost in the flood of new content, reducing retention.

❌ Engineering team lacks bandwidth to build and maintain search.

❌ No Web3-native search solution fits their needs without major customization.

---

Both Alex and Jordan understand the problem, but their pain points vary. As DSTOR scaled, we identified a third potential customer—Web3 newcomers—who would need more education and onboarding.

To further refine the product, we developed key user stories to define our focus:

📌 As an indie developer, I want to integrate search effortlessly, so my users stay engaged and find what they need.

📌 As a product lead at a scaling Web3 platform, I want a seamless search solution that improves retention without overloading my engineering team.

---

Figuring Out What to Build

To define what DSTOR should be and achieve, I needed to pinpoint the primary problem to solve. Based on my key persona’s challenges, I developed a clear problem statement:

Alex is an independent Web3 developer who needs a simple way to add search to his app because fragmented blockchain data makes it difficult for users to find relevant content.

This problem directly impacts user retention and engagement—without effective search, users disengage, reducing the long-term viability of many Web3 applications. Through my interviews and research, it became clear that this was a widespread challenge, not just a niche inconvenience.

"So how can I create a solution that’s seamless, scalable, and truly enhances the Web3 user experience?"

There were no direct competitors offering search solutions specifically for Web3 apps, but I could learn from established tools in adjacent spaces. I audited existing website search solutions, including Google Site Search, Algolia, and other SaaS-based search tools. I focused on their strengths—such as ease of integration, indexing speed, and customization options—while identifying gaps in how they handled decentralized and blockchain-based data.

To define what would make DSTOR truly valuable, I developed a unique value proposition that directly addressed my personas’ needs and frustrations:

🔍 Effortless search integration – Add powerful search without managing infrastructure.

📊 Optimized for Web3 – Index and search decentralized content seamlessly.

⚡ Real-time updates – Keep search results fresh as new blockchain data emerges.

🛠️ Customizable ranking – Developers can fine-tune relevance for their app’s needs.

🔗 Cross-chain compatibility – Support for multiple blockchain ecosystems.

With these core differentiators, I had a clear direction for the first version of DSTOR.

---

Scope and Structure

I limited the initial scope to a developer-friendly Web3 SaaS rather than a broad, all-purpose search tool. My goal was to make the implementation as seamless as possible—meaning clear API documentation, easy integration, and scalability across different blockchain ecosystems. This required structuring the product with practical constraints in mind: indexing needed to be efficient, queries had to be cost-effective, and user onboarding had to be frictionless.

With a clear goal statement:

DSTOR will provide Web3 developers with an easy-to-integrate search solution that enhances content discoverability and engagement. Success will be measured through developer adoption, search performance, and user engagement metrics.

I began brainstorming features by asking:

"How might we enable developers to add powerful search to their apps with minimal effort?"

I explored a wide range of ideas without constraints, allowing for creative solutions I might not have considered otherwise. This helped uncover the most effective approach for DSTOR.

Users could be guided through search integration with analytics on query performance or broader recommendations on optimizing content discoverability.

The most important action a new user could take is setting up search for their app—this is our primary flow. I mapped out a basic version, considering key configuration choices developers might need and defining a clear happy path, which allowed me to start structuring the onboarding experience.

graph TD;
    A[Open DSTOR] --> C[Dashboard]
    C --> D{Set up search?}

    D -- Yes --> E[Configure data source &<br />schema]
    E --> F[Choose indexing options &<br />Define search params]
    F --> H[Deploy & Test]

    D -- No --> C

    H --> I{Finished?}

    I -- No --> C
    I -- Yes --> J[Profit]

Product Development and Technical Challenges

With validation in hand, I developed a product roadmap focusing on key functionalities: real-time blockchain data indexing, advanced search capabilities, and seamless developer integration. One of the biggest technical challenges was automating customer onboarding. Since each Web3 application had unique data structures, ensuring a unified schema was critical for search functionality. I evaluated multiple solutions, including ETL pipelines and AI-powered data parsing, ultimately choosing Apache Airflow for its flexibility and scalability.

Securing funding was another critical step. To accelerate development, I pitched DSTOR to Forward Research, a Web3-focused investment group. My proposal was successful, leading to a pre-seed investment that enabled me to hire two engineers. With a team in place, we rapidly built and tested DSTOR’s core functionality, iterating based on feedback from early adopters.

Launch and Impact

By December 2023, DSTOR was ready for launch. Within 24 hours of going live, we secured our first paying subscriber. Within three months, we had onboarded seven customers and indexed over 30 million documents, significantly enhancing content discoverability for Web3 users. Our largest customer, Paragraph—a Web3 blogging platform—reported a jump from 3 to 5 unique page views per session. Across all clients, user retention improved by an average of 15% post-integration, demonstrating the direct impact of DSTOR on engagement.

Lessons Learned

DSTOR was more than just a search tool—it was a critical infrastructure solution for the evolving Web3 ecosystem. The journey reinforced the importance of identifying real pain points, validating demand, and iterating quickly based on user feedback. While DSTOR ultimately closed, its success demonstrated that user-centric innovation and technical execution can drive meaningful improvements in digital experiences. This experience shaped how I approach product development—focusing on solving high-value problems with scalable, user-friendly solutions.