A healthcare technology company
Customizable AI agents that extract user-defined terms and query hundreds of provider and payer contracts in seconds.
Thousands
Of contracts compared in one platform
Sub-second
Natural-language contract queries
Citation-based
Verification of every extraction
Healthcare organizations spent countless hours manually reviewing complex contracts to extract critical terms. Each had unique legal requirements and processes, making standardized extraction ineffective and creating operational bottlenecks.
Existing solutions could not adapt to different legal frameworks or custom term definitions. Organizations needed flexible extraction that could identify any user-defined term, from reimbursement rates to custom compliance clauses.
Each organization operates under a different regulatory framework and internal process, and needed customizable AI that could extract organization-specific terms while maintaining HIPAA compliance and ISO 27001 standards.
We built AI agents using AWS Bedrock with LangChain and LangGraph that let users define any extraction terms they need, adapting to their legal requirements without system changes. Users can specify custom fields, compliance clauses, or unique terminology and compare them across hundreds of contracts.
We implemented a citation system that shows the exact source location for every extraction, letting users verify accuracy against the original document. A Qdrant vector database supports organization-specific terminology, enabling natural-language queries with sub-second response times.
We built a Next.js and TypeScript frontend for reviewing extractions with side-by-side document viewing and highlighted citations, so organizations can configure their own terms while maintaining verification workflows for compliance and accuracy.
The platform enabled the company to open multiple commercial conversations with major health insurance providers, demonstrating market viability. Teams can review and compare thousands of contracts through one platform, sharply reducing review time across different regulatory environments.
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