Enhancing Content Creation and Personalization for Curly Tales with Generative AI

Curly Tales, a leading food, travel, and lifestyle storytelling platform, engages its audience with curated experiences and insights across cities. With a vast repository of user interaction data and content, Curly Tales sought to revolutionize its operations through GenAI-driven automation and personalization.

Business Problem

Manual content generation was time-intensive, limiting the platform’s ability to scale and personalize content.
Curating high-quality, contextually relevant images for articles posed significant challenges.
Delivering tailored travel itineraries and recommendations required substantial manual intervention, limiting user engagement potential.

Solution

GoML collaborated with Curly Tales to deliver a Proof of Concept (PoC) using outstanding Generative AI technologies. The solution addressed these challenges through the following components, supported by an advanced tech stack:

1.

Article Outline Generation
– Technology Used: Claude V3.5 (LLM Model) with Open Search Vector Database
– Developed a system with a rag pipeline to generate structured and detailed article outlines based on user input.
– Added source references

3.

AI-Powered Itinerary Planner
– Technology Used: Claude V3.5 (LLM Model) with Open Search Vector Database
– Built an API endpoint to generate personalized travel itineraries using Curly Tales’ extensive content database.
– Provided tailored recommendations to improve user satisfaction and engagement.

5.

Infrastructure
– Technology Used: AWS
– Ensured scalability, reliability, and seamless integration with Curly Tales’ existing systems.

2.

Image Generation
– Technology Used: Stable Diffusion V3 (Image Model)
– Leveraged AI to create high-quality, contextually aligned images that enhance the visual appeal and engagement of articles.

4.

Simple User Interface (UI)
– Technology Used: React
– Designed a lightweight and intuitive UI, enabling users to input topics and keywords and receive AI-generated article outlines and visuals effortlessly.

Architecture

Data Sources and Knowledge Base Creation
– AWS S3 Buckets: Stores raw data for processing.
– Data Mart: Aggregates and organizes data for further use.
– Knowledge Base Creation: Curates the data to form a centralized repository for vectorization.
Data Preprocessing
– Python Native Scripts: Executes preprocessing logic on raw data.
– Data Preprocessing Layer: Cleans, structures, and prepares data for vectorization.
Vectorization Process
– Business Intelligence Heuristics: Applies analytical rules for data transformation.
– Chucking and Rejoining Algorithms: Optimizes data chunks for embedding.
– Metadata Processing: Annotates data with relevant metadata.
– Embedding Model: Converts processed data into vector representations.
– Storage: Uses OpenSearch Vector DB for storing vectorized data.
Query Processing
– User Input: Accepts conversational queries from users.
– Query Processing Module: Analyzes and processes user queries to extract intent.
Advanced Retrieval-Augmented Generation (RAG) Pipeline
– AWS Bedrock: Powers foundational AI services and manages to embed them.
– Data Pipelines: Facilitates smooth data flow and transformation.
– Parameter Extraction and Generation Layer: Extracts parameters from queries for relevant response generation.
– Historical Chat Storage: Stores conversation history for contextual replies.
Focused AI Agents
– Multiple AI Agents: Specialized agents handle distinct functionalities based on query parameters.
Deployment and Infrastructure
– Code Repository: Centralized version control for all components.
– Docker: Containerizes applications for consistent deployment across environments.

Outcomes