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A mid-sized personal injury law firm in the United States that manages a high volume of accident, medical negligence, and insurance-related cases. Their team handles extensive documentation for each claim, including medical files, police reports, and insurance paperwork.
With growing caseloads and increasing pressure to deliver faster case assessments, the firm was looking for a scalable solution to streamline case preparation and improve internal workflow efficiency.
They needed a secure and compliant system capable of automating workflows—document processing, extracting meaningful insights from case files, and supporting attorneys with structured summaries and timelines.
Key needs included:
The goal was to reduce manual review time, improve accuracy, and equip their team with more tools to assess cases more efficiently.
We designed and implemented an IDP platform tailored to personal injury workflows. The solution combined OCR, NLP, and a fine-tuned version of Claude 3.5 Sonnet to automate documentation tasks and case interpretation.
To address challenges around data variability and model accuracy, we applied an iterative development approach, fine-tuned AI components on law-firm-specific datasets, and collaborated closely with their legal team for model validation.
Workflow of the Solution:
Documents such as PDFs, medical scans, emails, and images were uploaded into a secure interface. OCR (optical character recognition) was used to extract structured and unstructured text from diverse file formats.
We refined the Claude 3.5 Sonnet model using personal injury cases, enabling it to interpret medical records, identify policy details, and recognize accident-related events with higher accuracy.
We integrated NLP (natural language processing) modules to extract dates, diagnoses, events, and treatments, assembling them into a chronological case storyline for attorney review.
The system also had data visualization and reporting modules that generated concise summaries of incidents, claimant details, treatment patterns, and insurance information. Our experts also integrated predictive ML modules to provide treatment projection insights where applicable.
We created a role-based dashboard that displayed timelines, summaries, document metadata, and case progress indicators, enabling attorneys to manage all cases through a single interface.
Before delivering the final solution, we conducted extensive QA and testing, along with multiple UAT cycles, to ensure it aligned with the firm's operational workflows and met compliance requirements.
Automated sorting, tagging, and indexing of uploaded documents.
NLP pipelines that extracted key events, created treatment chronologies, and generated structured case summaries.
Custom dashboards showing document statistics, case milestones, and workload metrics.
End-to-end encryption, role-based access, and multi-factor authentication.
Secure APIs designed to establish communication with the client’s internal systems and document repositories.
The deployed IDP solution delivered measurable improvements across the firm's operations:
65% reduction in manual document handling time through automated sorting and text extraction.
30% faster case analysis enabled by shorter timelines and AI-generated summaries.
More consistent reporting and documentation, supported by standardized insights and dashboards.
The solution was delivered with a 100-day warranty, along with ongoing support, periodic enhancements, and continuous security updates.
For contextual understanding, document interpretation, summarization, and domain-specific reasoning.
For accurate extraction of text from scanned forms, handwritten notes, and medical records.
For event extraction, timeline creation, classification tasks, and predictive modeling for treatment analysis.
Since 1999, SunTec has been a trusted business process and software engineering partner for technology companies worldwide. Our team of over 1,500 full-time employees delivers innovative services every day to help our clients save time and money.
