30h / wk Support
NeuroForge Labs
AI Integration
THE CHALLENGE
Attorneys wasting hours searching document archives.
LegalMind's staff spent over 12 hours weekly searching through 100,000+ unstructured legal case records, slowing down citation research and decreasing client caseload capacities.
OUR SOLUTION
Pinecone Vector Indexing & Semantic Research Tool
We built a document chunking and vector indexing system utilizing OpenAI text embedding models. The interface enables attorneys to search case files semantically (by concept and context rather than exact keywords).
HOW WE DID IT
Our Execution Strategy
1
Asset Parsing
Converted PDF and case scan arrays into text nodes.
2
Chunking Engine
Coded text splitter logic to preserve concepts.
3
Embedding Sync
Generated text embeddings using OpenAI API.
4
Index Setup
Uploaded vector arrays to Pinecone servers.
5
UI Deployment
Coded responsive lookup panels in React.
PROJECT VISUALS
Client Interface Previews
[ INTERFACE PREVIEW 1 ]
[ INTERFACE PREVIEW 2 ]
[ INTERFACE PREVIEW 3 ]
TECH STACK
Core Technologies Implemented
OpenAI Text Embeddings
Pinecone Vector Indexes
Python PDF Parser
React UI Panels
Serverless Functions
"Case preparation speeds grew exponentially. Attorneys find key case precedents in seconds, saving up to 10 hours weekly."
LM
Arthur Pendelton
Senior Partner, LegalMind
★ ★ ★ ★ ★
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