Transforming Legal Workflows With Agentic AI
A custom AI platform helped a leading law firm automate legal research, document analysis, drafting, and repeatable legal workflows.
At a Glance
A unified LegalTech platform combined AI-powered Search, Chat, and task-specific Agents to streamline complex legal workflows.
Industry
Legal Services
Function
Legal Operations
Solution
Custom AI LegalTech Platform
Engagement
End-to-End AI Transformation
The Client
The client is a US-based, full-service business law firm ranked among the Am Law 200, with more than 1,000 attorneys across multiple cities. The firm provides legal services across corporate law, litigation, real estate, labor and employment, and intellectual property.
The Challenges
Moving From Fragmented AI Tools to Scalable Legal Workflows
For an Am Law 200 firm, adopting AI meant more than adding new tools. It meant building a foundation that could support complex legal work, scale across teams, and move beyond early-stage experimentation.
Large Document Processing Failures
Existing Custom GPT solutions struggled with 300–1,000+ page legal documents because of token limitations.
Unstructured AI Roadmap
Multiple GenAI ideas existed across the organization without a clear framework for prioritization and execution.
Single Point of Dependency
Product, architecture, and prompt engineering decisions depended heavily on one individual.
Limited Vendor Transparency
A ticket- and sprint-driven vendor model provided limited strategic input and long-term direction.
Manual Legal Workflows
Legal research, document review, analysis, and drafting involved time-intensive manual processes.
Performance and Scalability Requirements
Long-running AI tasks needed to run efficiently without blocking users or affecting the platform experience.
Build AI Workflows Around Your Legal Operations
Create a custom AI platform that connects legal research, document processing, drafting, and repeatable workflows in one environment.
The Solution
Building a Unified Agentic AI Platform for Legal Teams
Maruti Techlabs designed and implemented a custom AI-powered LegalTech platform using OpenAI models, asynchronous processing, caching, and multi-agent workflows.
Evaluated existing legal workflows, identified high-value GenAI opportunities, and prioritized them into a structured roadmap aligned with business goals.
Evaluated existing legal workflows, identified high-value GenAI opportunities, and prioritized them into a structured roadmap aligned with business goals.
The Journey
Manual to Agentic
Business Outcomes
From AI Experiments to Scalable Legal Workflows
95%+ Accuracy in Legal Reasoning
The platform achieved more than 95% accuracy across complex legal workflows.
7–8 Hours Reduced to Minutes
Tasks that previously required 7–8 hours could be completed in minutes across document review, research, and drafting.
Large Documents Processed Without Manual Chunking
The platform enabled processing of 300–1,000+ page legal documents without manual splitting.
Structured GenAI Roadmap
Fragmented AI experimentation was replaced with a prioritized, execution-ready roadmap.
Lower Legal Costs
AI-assisted execution reduced the amount of attorney time required for repetitive tasks.
Reduced Key-Person Dependency
Cross-training expanded decision-making across architecture, AI, and product functions.
Conclusion
The platform improved the firm's ability to process large documents, automate legal workflows, and scale AI adoption across teams.
Tech Stack
Create AI Workflows Built Around Your Legal Team
Connect research, document analysis, drafting, and specialized legal tasks through a centralized AI platform.
Download Your Free Case Study Today.
Frequently Asked Questions
The platform removed the previous document-size limitations and supported processing of 300–1,000+ page legal documents without manual chunking.
The Chat module supported legal research, contract review and drafting, document analysis, drafting assistance, and case strategy and analysis.
The Search module searched the firm's own documents and data to provide source-grounded results rather than searching the open internet.
Agents handled narrow, repeatable, high-volume workflows such as motion drafting, client-specific research, invoice extraction, and fee application analysis.
Long-running tasks were moved to asynchronous pipelines, while caching supported frequently accessed data without blocking the user experience.