A case study exploring how we built an AI-powered travel assistant using Ruby and AWS Bedrock, demonstrating how choosing the right tools over popular choices led to a more robust and maintainable solution. This study examines our approach to integrating AI capabilities within existing Ruby infrastructure while maintaining enterprise security standards.

AI-powered Ruby travel assistant

A technical case study detailing the implementation of an AI chatbot agent in a project management platform. Learn how the team leveraged LangChain, LangGraph, and GPT-4 to build a multi-agent system using the supervisor-worker pattern. 

Building chatbot agent to streamline project management

A technical case study of creating an automated system that downloads videos, processes audio, and generates transcripts using AI services like Groq and OpenAI.

Building data pipeline for OGIF transcriber

An in-depth look at Dwarves' monthly Project Reports system - a lean, efficient system that transforms communication data into actionable intelligence for Operations teams. This case study explores how we orchestrate multiple data streams into comprehensive project insights while maintaining enterprise-grade security and cost efficiency.

Project reports system: a case study

When building a chatbot agent, it's important to evaluate its performance and user satisfaction. One effective method is user simulation, which involves creating virtual users to interact with the chatbot and assess its responses. This approach allows for a more realistic evaluation of the chatbot's capabilities and user experience.

Evaluate Chatbot Agent by User Simulation

In AI integrated systems, instead of put all workload on a single agent, we can apply divide and conquer strategy to distribute workload to multiple agents. This approach can enhance task completion by leveraging the unique skills and capabilities of each agent.This approach allows for more complex and nuanced problem-solving, as well as increased efficiency and scalability. By coordinating and communicating effectively, agents can work together to achieve common goals, divide labor, and overcome challenges that a single agent might face alone

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