Automated Court Document Processing Solutions

Take your court’s document-processing procedures from manual, repetitive, time-consuming, and error-prone to automated and AI-enabled. Document Automation powered by CSI speeds up document processing time, expedites workflows, and reduces errors. This gives your court staff more time for meaningful tasks, and it improves your bottom line.

  • Enjoy automatic document classification, data extraction, redaction, and data entry

  • Maintain complete control with decision-making that’s rule-based and designed specifically for your court

  • Process and review court documents faster and more accurately leveraging a Tyler-hosted open source LLM server

  • Secure data in the AWS cloud with built-in hallucination protection

  • Prevent hallucination with additional layers of data validation

  • Trust in 30+ years of court technology and AI leadership experience

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Embracing the Possibilities of AI-Powered Automation

In a digital world, efficiency is about making the right technology... work for you. With the first and only AI solution built for courts.

Document Automation is Specifically Crafted for Courts

With AI-powered document understanding and automated data entry capabilities purpose-built for use in courts, Document Automation enables your electronic filings to be reviewed and docketed automatically within your case management system (CMS). Leveraging the latest and smartest large language models (LLMs), your court workflow is automatically prompted by system-ready engines to process and review documents exactly as they would have been by court staff, but in less time with consistent data-entry decisions and zero mistakes. Layers of data validation prevent hallucinations and have the potential to eliminate human in the loop reviews.

Efficient, Accurate Court Workflows

Just as a court clerk selects an electronic envelope to review and process — extracting key details for classification, identifying missing information, redacting sensitive data, and assigning to a docket within your CMS — Document Automation manages these steps for your court, eliminating repetitive tasks while improving accuracy by as much as 15-20% and expanding your processing hours.

  • Large language model (LLM) hosting server
  • AI optical character recognition (OCR)
  • AI document classification
  • AI data extraction and redaction
  • Robotic process automation for automated data entry

Enhanced AI-Powered Justice Experience

AI-powered automation of document review and data entry processes empower court staff to provide personalized attention to higher-value tasks, relieve stress from the burden of mundane tasks, and create more time to focus on face-to-face customer services. Lawyers, paralegals, self-represented litigants, as well as the public, all benefit from swifter access to court records. Legal requirements for timely filing review are easily met.

Improved Scale, Reduced Costs, and ROI

Document Automation provides quick access to reliable, scalable automations that can help fill workforce gaps, eliminate case backlogs, and achieve same-day document acceptance — all with proven client satisfaction, efficiency for the entire team, and a faster implementation timeline. For example, Palm Beach County, Florida, first implemented Document Automation in 2017, and they realized $1.9 million in annual savings by automating electronic filings.

Partnership You Can Trust

Document Automation, powered by CSI, is the first and only document-understanding, data extraction, redaction, and RPA solution purpose-built for this sector. The software has a well-credentialed, data-backed reputation of trustworthiness and can provide high quality control checkpoints. With 30 years of experience as a leader in court document AI automation, Tyler can ensure that AI is implemented in your court in a way that is safe, effective, and accessible for anyone on your team to use.

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Looking for valuable software connections related to Document Automation?

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Learn more about what it takes to implement AI in your court using a crawl-walk-run approach.

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