1 00:00:01,520 --> 00:00:06,560 Using artificial intelligence, specifically  computer vision and machine learning,   2 00:00:06,560 --> 00:00:11,520 it is possible to properly tag large  quantities of repetitive or similar PDFs   3 00:00:11,520 --> 00:00:16,880 and make them accessible in one seamless  process. Equidox will gather customer data   4 00:00:16,880 --> 00:00:21,040 and use machine learning to analyze the  documents and create a digital template.   5 00:00:21,040 --> 00:00:25,200 The template will be applied during the batch  processing to produce an accessible PDF.   6 00:00:25,760 --> 00:00:32,320 First, the customer or end user will request a  PDF file. For example, when a banking customer   7 00:00:32,320 --> 00:00:37,520 requests a PDF file of their monthly statement  from the bank's website. The requested file will   8 00:00:37,520 --> 00:00:43,040 enter the Rest API and be conveyed to either a  locally installed or a cloud server. This server   9 00:00:43,040 --> 00:00:48,000 will have the option of load balancing, allowing  it to be expanded as needed in times of greater   10 00:00:48,000 --> 00:00:52,960 demand. Within that cloud server, Equidox  will be running on a secure virtual machine.   11 00:00:53,520 --> 00:00:59,280 The PDF file will be deconstructed into individual  pages. Equidox will use machine learning to   12 00:00:59,280 --> 00:01:04,000 compare each page to the digital templates  that have been developed. Then Equidox will   13 00:01:04,000 --> 00:01:09,760 apply a zone map that will create digital tags  that can be understood by assistive technology.   14 00:01:09,760 --> 00:01:15,200 The tagged and remediated PDF is generated  and returned to the end user via the Rest API.   15 00:01:15,920 --> 00:01:22,720 With one click, the results will be accessible  documents. The time and manpower saved will be   16 00:01:22,720 --> 00:01:30,160 enormous. The end goal is a fast, seamless,  and secure process to achieving compliance.