Legal Document Analyzer

System designed for legal discovery workflows, combining large-scale document indexing with AI-driven summarization, categorization, and structured information extraction.

ElasticsearchEntity extractionAI summaries

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Summary

Project summary

AI-powered legal analysis system built to scale to millions of documents using Elasticsearch.

System designed for legal discovery workflows, combining large-scale document indexing with AI-driven summarization, categorization, and structured information extraction.

Problem

What needed to be solved

Legal discovery involves massive volumes of documents, unstructured text, and difficulty identifying key relationships and timelines.

Traditional approaches are time-consuming.
Traditional approaches are manual.
Traditional approaches are difficult to scale.

Approach

How it was built

Key implementation decisions, system behavior, and workflow structure.

Used Elasticsearch to index and query large document sets and support scaling to millions of records.
Applied AI to summarize documents, categorize content, and extract key entities.
Built structured outputs such as a timeline of events and a person and entity relationship map.
Focused on turning unstructured data into usable, structured insights.

Tech stack

Tools and platform choices

Core technologies used in the project.

Next.js
Supabase
Elasticsearch
TypeScript
AI APIs (LLMs, embeddings)

Tradeoffs and lessons

What mattered during implementation

Challenges, tradeoffs, and takeaways from the project.

Challenges / Tradeoffs

Scaling search and retrieval across large datasets.
Ensuring AI summaries are accurate and useful.
Extracting structured data from inconsistent text.
Balancing performance vs depth of analysis.

Outcome / Lessons

AI is most valuable when paired with strong search infrastructure.
Structured outputs such as timelines and relationships are more useful than raw summaries.
Combining search and AI enables scalable analysis systems.