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1. AI analysis of reservation notes and turn it into structured data

   Hi!
   Sometimes we receive a ton of information about the guest or the reservation via reservation notes from OTAs / TAs that are not structured and thus currently cannot be used to enhance the guest profile or reservation with structured data. LLM-AI analysis could potentiall understand the notes and make filter usable information and turn it into structured data.
   Example:
   Guest preferences: often times "high floor" "bath tub" "balcony" "quite room" "away from elevators" etc. are added to the reservation notes. matching those to the guest preferences available in profiles and space features before assigning a room would allow the system to align room asignment with guest preferences
   Arrival Time: sometime the approx. arrival time is indicated in the notes -> turn that into setting the arrival time accordingly
   Booking Purpose: set the booking purpose property if the AI can guess the purpose, like for example in case a company invoicing address is found in the notes, or an explicit purpose declaration
   Company: if a company name is found in the notes, associate the reservation with a company profile if one allready exists in MEWS

   I am sure there are further possibilites....

   Hi!
   Sometimes we receive a ton of information about the guest or the reservation via reservation notes from OTAs / TAs that are not structured and thus currently cannot be used to enhance the guest profile or reservation with structured data. LLM-AI analysis could potentiall understand the notes and make filter usable information and turn it into structured data.
   Example:
   Guest preferences: often times "high floor" "bath tub" "balcony" "quite room" "away from elevators" etc. are added to the reservation notes. matching those to the guest preferences available in profiles and space features before assigning a room would allow the… more

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   idea to upvote  ·  2 comments  ·  AI, budget/forecasting & advanced analytics  ·  Delete…  ·  Admin →
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