LLM-Driven Legal Document Summarization
AI solution for faster and more efficient legal document summaries.
AI development
Legal
2 months (+ongoing support)
Web
About the project
Our client is a mid-sized legaltech company that specializes in corporate compliance and contract management solutions for in-house legal teams. They approached us to build a custom solution that could automatically summarize long legal documents into concise and readable summaries.
We were responsible for
Wireframes and prototyping
UI/UX design
Frontend development
Backend development
LLM integration
Project Team
Project manager
Two ML engineers
Two backend engineers
One frontend engineer
DevOps engineer
QA engineer
UX/UI designer
Image
Image
Features in detail
Here is what the final solution consists of and what tasks it can complete.
Project timeline
Tech stack
What technologies did we use to create the solution?
Development challenges and solutions
How our team dealt with a range of development challenges.
Inconsistent document structures
Challenge: Legal documents come in wildly different formats, styles, and levels of complexity, which can be confusing for the LLM.
Solution: We used a structured parsing pipeline and standardized input formats.
LLM hallucinations
Challenge: LLMs can generate fluent but incorrect summaries, which is dangerous in legal contexts.
Solution: We applied retrieval-augmented generation (RAG) to ground responses in source documents and made it link each summary sentence to the source text.
Preprocessing framework for legal AI
The framework ensures raw data from various inputs is normalized, validated, structured, and enriched before being handed off to the LLM for summarization.
Input Sources
files, APIs, DB
Input Normalization
format check & conversion to standardized format
Validation & Preprocessing
schema check, cleaning, dedup
Structured Parser
extract fields, metadata tagging
Post-Processing
enrichment, error logging, data correction
Structured Output
JSON, XML, DB rows, or internal model
Result
We reduced legal document review time by 63%
Enabled junior staff to handle 2.5x more documents per day
Reduced human error and inconsistency rates by 85%