Accelerating Legal Review with AI for Efficient M&A Due Diligence

Delivering traceable, context-aware legal intelligence in a fully controlled, enterprise-grade environment.

DRL Team

AI R&D Center

8 min read

Accelerating Legal Review with AI for Efficient M&A Due Diligence

Content

Summary Tech Challenge Solution Impact

Summary

Legal due diligence in mergers and acquisitions (M&A) is traditionally manual, which is time-consuming and error-prone. Teams of junior lawyers review thousands of documents under tight deadlines. This creates bottlenecks, drives up costs, and limits a firm’s ability to scale.
To address this, we developed a domain-specific AI legal assistant that automates corporate document ingestion, classification, and review.
We deployed a custom LLM-based architecture, backed by a high-performance vector search engine. Our solution delivers context-aware summaries, risk insights, and extracts clauses, keeping all the links to the initial documents.
By automating up to 80% of manual review, the assistant drastically reduces time-to-insight, improves accuracy, and allows legal teams to focus on strategic advisory work rather than document triage.

Tech Stack

Python

PostgreSQL

Redis

OCR

SpaCy

Milvus

Sentence Transformers

Retrieval-Augmented Generation (RAG)

Prompt Engineering

Tech Challenge

Scalable Ingestion Pipeline. M&A data rooms contain hundreds to thousands of documents in varied formats. We needed a robust pipeline to handle OCR, metadata extraction, and content normalization quickly.
Clause-Level Understanding. The assistant must identify key legal clauses, extract obligations, and detect crucial risks or anomalies in documents.
Cited, Contextual Answers Only. Legal professionals require traceability. Every AI-generated summary or insight had to be grounded in retrieved source text, with no hallucination or unverifiable claims.
Custom Legal Taxonomies and Schema. NDAs, MSAs, board resolutions, and commercial leases all carry different review requirements. The system needed flexible, domain-aware tagging and search across contract types.
Enterprise-Grade Security and Deployment. Legal documents contain confidential and privileged information. Our platform had to support full encryption, fine-grained access controls, audit logging, and VPC or on-prem deployment options.

Solution

Documents are uploaded or synced via secure integrations. We use OCR, PDF parsers, and layout-aware NLP to extract structural metadata (dates, parties, signatures, etc.).
Clause-level embeddings are stored in Milvus, allowing the LLM to retrieve highly relevant passages for analysis and summarization.
A custom RAG layer ensures summaries and Q&A responses are grounded in retrieved text. Prompts are domain-tuned for legal accuracy.
Multilayer LLM-based architecture identifies and categorizes key clauses (e.g., indemnity, termination, governing law) and detects possible risks.
AI Assistant is equipped with tools for exploring summaries, clicking on citations, tagging documents, and exporting structured reports.
The entire solution is deployed within the client’s secure infrastructure ensuring that sensitive legal content is never exposed to external networks or cloud services.
The system operates without reliance on external APIs or third-party platforms, providing full data sovereignty and making it easier to meet strict regulatory, privacy, and client confidentiality requirements.

Consider a typical M&A deal where a law firm receives a virtual data room containing hundreds of corporate documents of different kinds: NDAs, employment contracts, complex supplier agreements etc.

A junior associate uploads the document archive. Within minutes, our AI solution automatically extracts metadata, identifies document types, and segments them by priority.

As they begin review, the associate is presented with AI-generated summaries of each contract, complete with highlighted risks (e.g., change of control clauses or missing signatures) and citations to the exact source text. They can click into a document, see the extracted parties and obligations, compare similar clauses across contracts, and export a structured diligence report to share with the partner.

What would’ve taken a full week of manual review now takes a few focused hours.

Impact

Significantly reduces the time required for due diligence, allowing legal teams to focus on higher-value tasks.
By automating routine tasks, law firms can lower operational costs associated with manual document review.
AI-driven analysis works in tandem with legal professionals**,** ensuring more reliable identification of potential risks in corporate documents.
Rather than replacing junior lawyers, the platform enables them to deliver higher-quality work, enhancing their value within firms.
According to pilot user feedback, the solution improved review accuracy by over 25% and reduced manual effort by nearly 40%.

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