We developed an AI model for the client that analyzes story descriptions and:
- Identifies thematic and plot similarities between titles
- Recommends books with a similar atmosphere, pacing, or setting
Following the success in the pilot phase (our model delivered the best results in a three-way competition), the model is now being gradually expanded, where:
- The AI works with full book texts, not just publisher-provided descriptions
- It extracts key elements of plot, characters, time, and setting that shape the reading experience
The resulting recommendations operate with a much broader data context than traditional algorithms.
„From the very beginning, the solution from Insynaps showed a deep understanding not only of technology, but of books themselves. The recommendations weren’t just accurate – they resonated with readers. That’s why we’re moving forward with them on the production version.“
Martinus
- Our solution delivered the best recommendations according to evaluations from both the client and their customers.
- Development of a solution that offers reader-relevant recommendations, not just statistical matches.
- A step toward AI systems capable of truly “understanding” literature – including the ability to suggest similar stories based on complex elements like theme, tone, and writing style.
- Expected impact: increased conversion rates from recommendations, longer time spent on site, and higher customer satisfaction with book selection.