DealyDigest

Inspiration

When Quora plateaued, its Daily Digest revitalized growth by delivering curated, relevant content. Inspired by this, we built DealyDigest—a financial tool that helps users maximize their spending by offering tailored deal recommendations based on their transactions and credit card usage. In an era of inflation and financial instability, our goal is to ensure every user optimizes their financial decisions effortlessly.

What it does

DealyDigest uses the Knot SDK to analyze recent authenticated transactions and credit card usage, identifying opportunities where users can save money or earn rewards. By leveraging this data, it recommends personalized deals on products and services that align with the user’s spending patterns, ensuring they get the most value from every purchase.

How we built it

We integrated the Knot SDK to authenticate transactions securely, built a backend system to process spending data, and implemented an AI-driven recommendation engine to surface the most relevant deals. Our front-end delivers a seamless user experience, allowing users to track and act on optimized spending opportunities effortlessly.

Challenges we ran into

The financial world is inherently fragile, with constantly changing security protocols, API restrictions, and privacy considerations. Handling sensitive financial data while ensuring compliance and usability was a major challenge. Additionally, optimizing recommendation accuracy without overwhelming users with irrelevant offers required fine-tuning our filtering algorithms.

Accomplishments that we're proud of

We successfully built a functional prototype that integrates secure financial data processing with real-time, personalized deal recommendations. Overcoming API limitations and ensuring smooth user authentication was a significant achievement. We’re particularly proud of our dynamic recommendation system, which adapts to user spending habits over time.

What we learned

We gained valuable insights into the complexities of financial data integration, security protocols, and API-driven development. Additionally, refining our recommendation model taught us the importance of balancing relevance with user experience, ensuring that users receive actionable insights without unnecessary noise.

What's next for DealyDigest

Next, we plan to implement an advanced filtration system, allowing users to refine recommendations based on categories, spending habits, and personal preferences. Additionally, we aim to introduce directed search, enabling users to look up specific products or services to find the best available deals. Expanding our credit optimization capabilities to suggest better card usage strategies is also on the horizon.

Built With

• auth0
• gemini
• knot
• mongodb
• nextjs
• shadcn
• tailwind
• typescript