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Case Study: Building a Knowledge Library for Hotel Superbrain's AI

  • Client: Hotel Superbrain (USA-Based AI Startup)
  • Role: Freelance Data Specialist
  • Timeline: June, 2025 (15-Day Contract)
  • Tools: Google Search, ChatGPT, Google Sheets

The Goal

Hotel Superbrain needed to build a foundational knowledge library for its generative AI. My task was to execute targeted research queries, gather relevant documents (PDFs), and transform the unstructured information into a clean, organized dataset of over 2,500 records that the AI could learn from.

Workflow diagram showing the process from query to data handoff
My systematic process for converting raw information into a high-quality, structured dataset.

My Contribution: A 3-Step Workflow

  1. Sourcing & Collection

    Based on specific query tasks, I performed advanced searches to identify and collect high-authority documents. The focus was on quality and relevance to ensure the AI was trained on expert-level information.

  2. AI-Assisted Extraction & Summarization

    This was the core of the process. For each document, I leveraged ChatGPT to extract key data points and synthesize complex information into concise, structured formats, dramatically increasing efficiency and consistency.

  3. Structuring & Handoff

    I meticulously populated the extracted data into a shared Google Sheet. A critical feature I included was a column with a direct download link for each source PDF, which optimized the data ingestion workflow for the engineering team and ensured a seamless project handoff.

Anonymized screenshot of the structured data in a spreadsheet
An anonymized look at the final dataset, structured for easy ingestion by the AI model.

The Outcome

The final deliverable was a robust, well-organized knowledge library of over 2,500 records. This work served as a critical building block for the Hotel Superbrain AI, enhancing its reasoning capabilities and enabling the team to accelerate the development pipeline.