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  6. [For Travelers] Itinerary Creation Support by LLFarM

[For Travelers] Itinerary Creation Support by LLFarM

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LLFarM

last updated:Mar 11, 2026

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Efficiently plan your trip with generative AI.

In the travel industry, there is a demand for flexible itinerary creation tailored to the needs of individual travelers. In particular, it is important to streamline complex tasks such as gathering information from diverse sources, optimizing transportation options, and selecting accommodations. LLFarM utilizes generative AI to address these challenges. 【Usage Scenarios】 - Information gathering in the initial stages of travel planning - Suggestions for transportation and accommodations - Itinerary optimization 【Benefits of Implementation】 - Reduction in time spent on travel planning - Creation of personalized itineraries based on diverse information - Increased traveler satisfaction

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[For Travelers] Itinerary Creation Support by LLFarM

LLFarM_ChatGPT Image 2026年1月14日 22_04_19.png
LLFarM_ChatGPT Image 2026年1月14日 22_04_19.png
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【Features】 - Supports a variety of documents including PDF, Excel, Word, and images - Performs preprocessing based on the content and layout of documents - Extracts and analyzes information with high accuracy, including tables, forms, and text within images - Searches and references internal documents, manuals, specifications, etc., using RAG - Builds a system where generative AI provides answers based on evidence - Assists with information retrieval, summarization, inquiry response, and document organization through AI-Agent 【Our Strengths】 Our company develops contract-based projects and our own products related to artificial intelligence. Since shifting to Deep Learning in 2013, we created a proprietary deep learning framework in C language in 2014. In July 2016, we successfully implemented the Double-DQN model in the All-Powerful Architecture Development Department. Since 2017, we have developed AI systems for major company N. Subsequently, we have developed many AI systems in fields such as manufacturing and healthcare. Awards: 2016: Winner of the 1st Artificial Intelligence Hackathon at Media Workshop 2018: Silver Medal at the Ocean Research and Development Agency Data Analysis Competition 2020: Bronze Medal at the Ministry of Economy, Trade and Industry Data Analysis Competition AI Edge Contest

Price information

- Prices will be individually estimated based on the content and requirements of the target business. - Prices vary depending on the types and quantities of documents handled and the necessary AI functions. - Adjustments can be made based on the cloud configuration and the presence or absence of UI development. - We respond flexibly from PoC and prototype development to full-scale implementation. - We propose the optimal configuration based on your requests and budget. - Please feel free to contact us for more details.

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※The delivery time may vary depending on specifications and volume, but we can respond promptly.

Applications/Examples of results

Case 1: Development of a Secure System Using Local LLM To handle business information that cannot be shared externally, we developed a secure AI system utilizing Local LLM. It was built with a user-friendly UI, assuming internal use. Case 2: Comprehensive Development Including RAG, LLM, and UI We designed and developed a long-term business support system that combines customer-specific data with RAG, frontier LLMs like GPT, and UI development. Case 3: High-Precision Reading of Special Documents By extracting tables, text, and images from special PDFs, forms, and image documents, and combining preprocessing, we achieved high-precision document analysis using LLM. *For more details, please download the PDF or feel free to contact us.*

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LLFarM Introduction and Development Case Studies

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Our company is engaged in various artificial intelligence-related businesses, including artificial intelligence development, artificial intelligence consulting, artificial intelligence seminars, and artificial intelligence application development. Since 2013, we have shifted to Deep Learning, working on improvements to CNNs and RNNs, and by 2015, we developed our own model using RNNs. Additionally, starting in 2016, we expanded our focus to deep reinforcement learning, developing numerous simulation models and robot models. Please feel free to contact us if you have any inquiries.

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