LawPilot AI - AI That Thinks Like a Lawyer
This application will assist law firms in managing legal documents, streamline the document creation and editing process, and facilitate client interaction. Lawyers can create, edit, and chat with legal documents. Clients can read, sign, and chat with the documents. AI features include summarizing legal documents and parsing uploaded agreements for searchability.
Detailed Requirements
- Multitenancy: Each law firm (tenant) has its own isolated data to ensure data security and segregation.
- User Management: Lawyers and clients can belong to multiple tenants.
- Document Management:
- Lawyers can create and edit legal documents.
- Lawyers and clients can chat within the document.
- Documents can be shared with clients.
- Client Interaction:
- Clients can read, sign, and chat with legal documents.
- Case Management: Track cases within the firm, assign cases to lawyers and clients, and maintain case details.
- Contract Management: Track legal contracts associated with cases, including the ability to upload, create, and summarize contracts.
- Contract Status: Track the status of contracts (created, read, signed, revoked).
Postgres Schemas
1. lawyers
Tracks information about lawyers, including their education and experience. The lawyers are users of the system and belong to a specific tenant. They have to be one of the users registered in the system.
2. clients
Tracks information about clients, including their personal information. The clients are users of the system and belong to a specific tenant. They have to be one of the users registered in the system.
3. cases
Tracks all cases within the firm, including assigned tenant, lawyer, and client. The cases have detailed description about the case and both lawyers and clients would want to ask questions about it. The vector embeddings are created for each of the case descriptions that are used to implement a RAG architecture to enable search capabilities. The AI can also study new cases based on past cases and propose next steps.
4. contracts
Tracks all legal contracts relevant to a case. Contracts can be uploaded or created. These contracts are typically really long and would be a great use case to help summarize and also ask questions. The embeddings are calculated for each of the contracts. For simplicity, this schema calculates one embedding per contract. In a real world application, you would want to create an embedding per contract chunk. This means breaking the contract into chunks and creating a table that tracks embeddings per chunk. These chunks can then be fed into the AI model for Q&A or summarization.
5. contract_status
Tracks the status of contracts (created, read, signed, revoked).
Complete SQL Script
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