Tikrly

Invest With Context, Not Guesses

Comment

Inspiration

Investing can feel overwhelming, especially for beginners. Many platforms provide large amounts of data but don’t clearly explain what that data means or how it applies to an individual user. I was inspired to build something that bridges this gap by helping users make more confident decisions by connecting stock insights directly to their personal goals and risk tolerance.

What it does

Tikrly is a beginner-friendly stock research web app that recommends stocks based on a user’s preferences. Users answer a short set of questions about their investment horizon, risk tolerance, goals, and experience level. Based on this, Tikrly generates personalized stock recommendations with trending news updates, a concise summary on why it suits your preferences, volatility and sentiment score based on data. If you have further questions or want more insights, Tikrly’s Agent is there to assist. Users can also search any stock and interact with an AI-powered agent that provides contextual, easy-to-understand insights.

How I built it

I built Tikrly using a modern full-stack web architecture: Frontend: Next.js with TypeScript and Tailwind CSS for a responsive and clean UI Backend: Next.js Route Handlers for serverless API endpoints Database: Supabase to store user data and preferences AI Integration: Gemini 2.5 Flash to power the personalized stock agent Data Sources: Yahoo Finance for stock data and NewsAPI for recent news context I also implemented a preference-based scoring system to match users with relevant stocks.

Challenges I ran into

One of the main challenges was designing a recommendation system that felt meaningful within a short timeframe. Translating user preferences like risk and goals into stock suggestions required careful simplification. Another challenge was integrating multiple data sources and ensuring the information presented remained clear and beginner-friendly rather than overwhelming. I also had to balance building a functional backend with delivering a polished user experience under tight time constraints.

Accomplishments that I’m proud of

I’m proud of creating a complete, working product with a clear user flow from onboarding to personalized recommendations to interactive stock exploration. I also successfully combined personalization with explainability, ensuring users not only receive recommendations but understand why those recommendations were made. Additionally, integrating an AI agent that provides contextual responses tailored to both the user and the stock adds a unique and impactful layer to the experience.

What I learned

I learned how important it is to prioritize clarity over complexity, especially when designing for beginners. Building intuitive flows and simple explanations can make a huge difference in user experience. I also gained experience working with full-stack tools, integrating APIs, and connecting frontend interactions with real backend data. Finally, I learned how to scope effectively and focus on delivering a strong, polished core feature set within a limited time.

What's next for Tikrly

I plan to expand Tikrly into a more comprehensive investment assistant by adding portfolio tracking, real-time alerts, and more advanced personalization that adapts to user behavior over time. I also aim to enhance the AI capabilities to provide deeper insights, comparisons between stocks, and more proactive recommendations to support long-term investing decisions.

Built With

Share this project:

Updates