How a Nonprofit Modernized Guardianship Case Intake with AI
Challenges
1- Manual legal document data entry.
2- Inconsistent Salesforce updates.
3- No automation for court form preparation.
4- Delayed case setup and filings.
5- Secure AI processing requirements.
Solutions
1- Private SLM-based document parsing.
2- Automated Salesforce field mapping.
3- Dynamic task and template generation.
4- Chatbot-enabled case data queries.
Results
1- 90% reduction in manual processing time.
2- 92% data extraction accuracy.
3- 85% faster case setup.
4- 95% secure, audit-compliant automation.
The organization provides legal guardianship and case management services for elderly individuals unable to manage their personal or financial affairs.
The intake process relied on manual review and data entry from multiple legal documents into Salesforce. This approach was time-intensive, error-prone, and lacked consistency, leading to delays in case setup and court filings while handling highly sensitive legal and medical data.
The client required a secure AI-driven automation framework capable of extracting structured data from guardianship documents, mapping that information directly into Salesforce fields, and dynamically generating follow-up tasks. The solution also needed to ensure secure AI processing using a private SLM environment while automating creation of standard court forms to accelerate filing timelines.
We developed a Salesforce-integrated AI Intake Bot powered by a private Secure Language Model (SLM) and a structured document processing pipeline. The system automatically identifies uploaded guardianship document types, extracts key legal details using AI models trained on guardianship data, and maps the extracted information directly into Salesforce records in real time. It also generates court-ready templates such as Oath & Designation, Commission Drafts, and Bond Applications, while dynamically creating context-based follow-up tasks. All AI processing runs within a secure SLM architecture designed to safeguard sensitive legal and medical information.
- Data Sensitivity and Compliance- Sensitive legal and medical data required secure and compliant AI processing.
- Unstructured Legal Documents- Guardianship documents arrived in various formats (PDF, Word) without consistent structure, making automated parsing complex.
- Incomplete Salesforce Field Mapping- Key fields such as medical summaries, case summaries, and evaluator details were not structured within Salesforce, limiting automation potential.
- Manual Task Creation- Staff manually created follow-up tasks for pension lookups, bond filings, and Social Security submissions.
- Time-Intensive Document Drafting- Court documents such as Oath & Designation and Bond Applications required manual drafting for every new case.
- Private SLM-Based Document Parsing- Developed a secure AI document processing pipeline powered by a private Secure Language Model to extract critical guardianship data including date of birth, judge details, granted powers, and evaluator information.
- Salesforce Integration and Structured Field Mapping- Mapped extracted data directly into Salesforce and introduced structured fields for medical summaries, court details, and case-specific information.
- Automated Task Generation- Configured logic to dynamically generate follow-up tasks such as Social Security filings and bond submissions based on extracted document data.
- Automated Legal Template Generation- Enabled automatic creation of court-ready documents—including Oath & Designation, Commission Drafts, and Bond Applications—using extracted case information.
- Chatbot Interface for Data Querying- Implemented a Salesforce-integrated chatbot interface powered by the private SLM to allow staff to query extracted case data without reviewing original documents.
90%
Reduced Manual Workload
Automation eliminated repetitive data entry and significantly reduced manual processing time.
92%
Improved Accuracy
Secure AI extraction powered by the private SLM achieved over 90% accuracy in updating Salesforce records.
85%
Faster Case Setup
Automated data mapping and document generation reduced intake timelines from hours to minutes.
88%
Dynamic Task Creation
Real-time task generation ensured timely follow-ups and improved case management efficiency.
95%
Scalable & Secure Operations
The private SLM architecture supports high document volumes while maintaining encryption, audit compliance, and controlled data access.
