Building an AI Research Engine for Complex Legal Questions
A custom deep research platform combined live search and agentic reasoning to turn complex legal questions into structured, source-backed reports.
At a Glance
The firm needed to handle complex research questions faster while giving attorneys reliable, traceable information they could verify and use confidently.
Industry
LegalTech
Function
Research & Strategy
Solution
Agentic Deep Research Platform
Engagement
End-to-End AI Solution Development
The Client
The client is a national law firm with 475 attorneys and government relations professionals across 17 offices. The firm serves regional, national, and international clients, including more than 40% of Fortune 500 companies, across complex legal, regulatory, and business matters.
The Challenges
Moving Beyond Search Results to Defensible Research
Complex legal and business questions required teams to gather information from numerous sources, assess context, compare findings, and establish a reliable trail back to the evidence.
Fragmented Research Workflow
Attorneys and analysts moved between search engines, internal documents, research tools, and notes to build a complete answer.
Heavy Research Burden
Complex questions required reviewing numerous sources, comparing perspectives, and connecting findings before forming a useful conclusion.
Limited Depth From Existing Tools
Search engines returned source links, while basic AI tools often lacked the contextual depth needed for complex legal research.
Weak Source Traceability
AI-generated answers without clear citations made it difficult for teams to verify claims before relying on them.
Inconsistent Research Quality
Research depth, structure, and completeness could vary depending on who did the work.
Growing Demand for Research
Increasing volumes of complex queries made it difficult to maintain research speed and consistency without adding manual effort.
Turn Complex Legal Questions Into Reliable Research
Give legal teams a research workflow that explores multiple sources, connects relevant findings, and shows exactly where each insight comes from.
The Solution
Creating a Research Engine That Goes Beyond Search
Maruti Techlabs developed an agentic deep-research platform that breaks complex questions into research tasks, investigates multiple sources, and assembles findings into verifiable reports.
The system breaks complex questions into smaller research tasks, explores different paths, and refines its approach as new information emerges.
The system breaks complex questions into smaller research tasks, explores different paths, and refines its approach as new information emerges.
The Journey
Research to Verified Insights
Business Outcomes
Smarter Research, Stronger Evidence, Faster Decisions
60% Less Research Time
A single automated workflow replaced hours of searching, reading, comparing, and manual synthesis.
3x Greater Research Depth and Coverage
Agentic exploration allowed the system to investigate multiple angles and sources instead of relying on a limited set of results.
90% Source-Backed Outputs
Every generated report includes citations and references, giving users a clear path back to the underlying information.
Greater Decision Confidence
Structured findings with supporting evidence gave teams more clarity when evaluating complex legal, regulatory, and business questions.
Consistent Research Quality
A standardized research process reduced variations in output quality between users, teams, and offices.
More Predictable Turnaround
Complex research requests could be completed through a defined workflow, reducing uncertainty around lengthy manual investigations.
Conclusion
The deep research platform changed how the firm approached complex information gathering, making research more thorough, traceable, and reusable.
Tech Stack
Give Your Teams a Smarter Way to Conduct Deep Research
Build an AI research workflow that can investigate complex questions, connect information across sources, and deliver findings with clear supporting evidence.
Download Your Free Case Study Today.
Frequently Asked Questions
It breaks broad queries into focused sub-questions, researches them independently, and combines the findings through agentic reasoning.
Each insight is linked to supporting sources through inline citations, allowing users to trace findings back to the underlying evidence.
Instead of returning a single response, the system plans research, explores multiple sources, evaluates findings, refines queries, and synthesizes the results.
Yes. Research parameters can be customized based on the query, source requirements, depth of analysis, and expected report structure.
A process view shows the research progression, while source citations provide traceability for the information used in the final report.