A Foreword by Stanford CodeX Associate Director, Dr. Megan Ma

Prototyping has historically enabled scholars to approach complex problems and translate theoretical concepts into practical solutions. This hands-on methodology bridges the often-wide gap between abstract ideas and real-world applications, allowing researchers to test, refine, and validate their hypotheses in tangible ways. By creating functional models or early-stage products, academics can now engage more directly with industry stakeholders, end-users, and policymakers, fostering a dynamic feedback loop that accelerates innovation and deepens the impact of scholarly work. More important than ever, prototyping is necessary for AI research in the legal space. Building on Codex’s insights on leveraging generative AI for the legal profession, Specter serves as a prime example of how prototyping bridges the gap between academic research and industry practice, demonstrating the crucial role of hands-on development in translating theoretical concepts into practical legal tools.

Adversarial Anticipation: Real-Time Simulated Negotiation

Aksh Garg, Stanford CS and CodeX Collaborator

Legal professionals often face the challenge of sifting through incredibly large volumes of past precedent to build strategies for their legal workflows. Each case also has its own unique idiosyncrasies, creating incredible dynamism in the process and encouraging highly context-dependent negotiations. To make navigating this broad search space easier, we developed Specter this past week: an AI web platform designed to help lawyers and legal professionals prepare for case negotiations by simulating potential outcomes.

Specter has three main components: (1) fast and efficient retrieval over past precedent, (2) parallel simulations of case outcomes determined by two agents negotiating against each other, and (3) a real-time voiceBot that attorneys can practice negotiating against.

Our retrieval engine leverages Anthropic’s contextual retrieval approach, which allows us to index every small piece of a legal document within the context of the larger document. To get started with precedent search, users can simply upload their knowledge libraries to the digital, indexable, and retrievable store (which we call the Vault). Once uploaded, our algorithms index the documents in parallel. After the documents finish indexing, lawyers can upload a case they are working on. At this point, our search engine looks at all indexed documents in the Vault, retrieving the most relevant ones to expedite the search process.

Next, we carry out trajectory simulations. Each simulation involves two Lawyer Agents – one representing firm A and another representing firm B – negotiating the deal with one another until they arrive at a consensus. By launching hundreds of these negotiations in parallel, our agents can discover more complex and intelligent strategies, leaving them better prepared for difficult arguments that the opposition might present.

Once the simulated negotiations are complete, insights from them are used to seed our opposition persona, which lawyers can talk to and negotiate against in real time. This opposition persona draws from the most successful strategies observed during the simulation stage, significantly elevating its abilities as a legal assistant. As lawyers approach their next case or negotiation, they can repeatedly practice with this in-house “opposition” representation until they feel ready.

Specter streamlines the preparation process for legal negotiations, combining advanced retrieval, realistic simulations, and interactive practice to provide a powerful toolkit for law firms.

View the Demo Video here: