legalgain is an AI-native legal research platform that deploys a coordinated team of research agents to deliver outcomes. Backed by Benchly and Servient, legalgain pairs Servient’s legal domain-specific AI engine with Benchly’s commercial-grade case law database to execute agentic legal research the way experienced legal teams would staff a complex matter.

This framework allows legalgain to coordinate research through comprehensive and structured data, domain-trained AI, and specialized agents working together toward a defined outcome. Rather than selling access to a database, the platform is designed to deliver finished legal work product aligned to an attorney’s objective, ultimately supporting a more flexible, outcome-oriented approach to legal research.

A Coordinated Team of Research Agents

legalgain operates through a coordinated team of research agents that work together like associates staffed to a matter:

• Supervising Agent
  Identifies the legal issues presented by the attorney and develops a research plan for review before any work begins.
• Research Agent
  Finds and distills the relevant case law and authority based on the approved research plan.
• Verification Agent
  Confirms that cited authority exists and ensures it is applied in the correct legal context.
• Synthesis Agent
  Pulls together the research and analysis into finished legal work product aligned to the attorney’s original issues.

AI-Native by Design

legalgain is built as an AI-native system from the ground up. It does not rely on a single model or chatbot to generate responses. Instead, legalgain coordinates multiple specialized agents, each responsible for a distinct part of the legal research process. This architecture allows research to be planned, executed, verified, and synthesized in a structured way that mirrors how legal work is actually performed.

By designing legalgain around agent collaboration rather than prompt-based interaction, the platform delivers explainable and traceable work product. Attorneys remain in control of the issues being researched and the outcomes being produced, while the AI handles the research execution.

Built on Commercial-Grade Legal Data

legalgain is built on a commercial-grade corpus of case law curated to support accurate legal reasoning, avoiding reliance on fragmented open-source collections. With the legal content being graphically abstracted to represent a comprehensive map of the law, legalgain’s AI engine can connect areas of the law relationally. This structure allows legalgain’s agents to understand legal context, apply authority correctly, and reduce the risk of contextual mismatches or fabricated citations.

Legal Research Built Around Outcomes

Traditional legal research tools are built around search, requiring attorneys to query databases and assemble analysis to reach an answer. Legal gAIn reverses that model by starting with the outcome the attorney needs and coordinating research around that objective. Rather than returning lists of cases or citations, the platform delivers finished legal work product aligned to the issue presented, allowing agents to plan, analyze, and synthesize authority with purpose. This outcome-driven approach reduces time spent searching while preserving the attorney’s role in applying judgment and strategy.

Pricing Aligned to Research Outcomes— Not Access

legalgain shifts legal research away from a fixed subscription that enables access to a database toward a consumption-based approach tied to the outcomes of the research being performed. Instead of paying for the ongoing capability to search, legal teams execute research at a project level when needed and are only billed once finished work product is delivered.