Dwey.

The problem

Library patrons discover books through rigid, keyword-based catalog search that returns poor matches and offers no semantic understanding of theme, tone, or pacing. There's no recommendation tool to discover reads by what someone actually likes, no user ratings to judge a book's quality, and the family library account can't track an individual reader's history or maintain a member-specific list.

General discovery tools like Goodreads, StoryGraph, and BookTok focus on popularity and live outside the library, disconnected from whether a title is actually available nearby — leaving patrons to bridge that gap manually.

The solution

Dwey (Doo-ee) is an AI library assistant that lets readers discover books through natural language and voice instead of keyword search. Patrons describe what they want — or ask for something like a book they loved — and receive age- and genre-appropriate recommendations filtered to titles available in their county's libraries.

Each recommendation shows title, author, cover, genre, availability, nearest library and proximity, metadata, and a sourced user rating, with a short personalized explanation. Patrons can act by voice: add to wishlist, place a hold, or reserve a checked-out copy. Profile-based accounts track each family member's reading history to sharpen future recommendations.

How it works

Dwey uses an OpenAI GPT-5.5 model for recommendation reasoning and light agentic tool-calling (holds, wishlist, ratings), with OpenAI's Web Speech API for speech-to-text and gpt-4o-mini-tts for text-to-speech. A vector database of 50–75 books enables semantic retrieval; user and library data live in SQLite, and a FastAPI service pulls ratings and rating counts from the Google Books API.

The system prompt casts the assistant as a librarian speaking in first person, adapting tone to the reader's age and prioritizing age, genre, availability, then proximity within a 10-mile radius. Strict guardrails require using the reviews tool for every recommendation and prohibit recommending books outside the inventory, outside the genre without asking, already read, or fabricated. A manual golden-set audit found an 80% baseline success rate; the main failure mode — ignoring a lower age floor and repeating titles on "more" requests — was fixed via a persona-first prompt structure and a check-before-guessing rule.

Who it's for

The business is B2B SaaS, sold to library systems on tiered annual licensing scaled by branches or cardholders, with a future affiliate revenue stream for titles the library doesn't carry. Buyers span public, academic, K-12 school, special, and national/state libraries.

End users are the patrons those libraries serve: general community members (children via parents, youth, adults, seniors), students and faculty, and professionals in legal, medical, and government fields. The assistant adapts recommendations and tone across these age groups and reading needs.

Why it matters

Libraries are in a wave of modernization, with cities and counties committing multi-million-dollar capital packages to renovate branches and add technology, fueled by federal, state, and local bond funding. Dwey positions itself as "Librarian-as-a-Service" — an intent-based discovery layer that improves patron engagement and maximizes collection utilization across roughly 9,000 U.S. public library systems and tens of thousands of school libraries.

Unlike popularity-driven competitors, Dwey combines semantic understanding with real library availability. Launch targets a limited pilot with a county library cohort to refine the recommendation logic before public release, with an evaluation roadmap moving toward assertion testing against the live catalog and LLM-as-a-judge grading against a 50+ persona golden dataset.

At a glance

Project

Dwey

Built by

Rushina Bhansali

One-liner

Dwey is an AI library assistant that helps readers discover books through natural language and voice instead of rigid keyword search.