An AI-driven document processing system automating title commitment data extraction and validation for Skylark.
Title and settlement services require precision and speed to ensure accurate commitment documentation and seamless real estate transactions.
Skylark, a title insurance solutions provider, specializes in commitment typing services that support mortgage and title industry workflows.
However, the company faced growing complexity in managing document validation, property data extraction, and scalability across a large volume of files.
Skylark delivers end-to-end commitment preparation support through a skilled workforce, helping title companies and mortgage institutions efficiently process real estate transaction documents.
The company's focus is on maintaining high accuracy in extracting critical property, ownership, and legal references from title documents—many of which arrive in varied formats from disparate sources.
Manual review was becoming unsustainable as volumes grew, necessitating an intelligent, automated solution.
Every operational bottleneck reported was matched to the workstream(s) that resolved it.
Retrieving property, party, and reference data from scanned or physical documents was slow and error-prone.
Integrated optical character recognition (OCR) and Large Language Models (LLMs) to auto-extract document type, involved parties, property details, and legal references from a wide variety of scanned title documents.
Title documents varied in layout and format, making it hard to enforce consistency during validation.
Skylark's internal teams used a validation interface to compare AI-extracted data with original source files, enabling manual corrections where needed.
Errors in extracted information directly impacted title insurance eligibility and risk evaluations.
Skylark's internal teams used a validation interface to compare AI-extracted data with original source files, enabling manual corrections where needed.
Built-in flags allowed reviewers to raise re-scan requests for low-quality or incomplete documents, preserving data integrity.
Managing documents across platforms such as OneDrive, Google Drive, and FTP servers created workflow bottlenecks.
Seamless synchronization was established with major cloud platforms including FTP, Google Drive, and OneDrive, ensuring real-time file availability across systems.
As document volume increased, the need for manual validation reduced throughput and delayed delivery timelines.
Documents were pre-validated and automatically queued based on extraction confidence levels and predefined rules, accelerating verification cycles.
With reduced manual review effort and consistent data handling, the system scaled effectively to manage larger client volumes.
Automated data extraction accelerated turnaround time and reduced errors in title commitment preparation.
Teams could review and validate high-confidence AI results faster, reducing document cycle times.
Integration with cloud platforms eliminated the friction of managing files across disconnected systems.
The automation backbone allowed Skylark to onboard more clients and handle peak workloads without expanding its operational footprint.
Validation and re-scan features ensured that only complete, accurate document data was used in final outputs.
