Introducing Custom Transaction Systems

Legal AI is on the cusp of a major transition. Most law firms have spent the past year adopting the same handful of legal AI products, and two uncomfortable truths are setting in.

The first is that these products are limited, especially on complex workflows like end-to-end transaction execution.

The second is that when every firm's output runs through the same off-the-shelf tools, clients struggle to tell one firm's work from another's. Over time, firms risk becoming interchangeable service layers on top of the same underlying technology.

Leading firms have taken note. Kirkland & Ellis's recent partnership with Palantir to build a proprietary fund formation engine is the clearest example yet. More will follow.

That's why today we're launching Custom Transaction Systems.

Our team of transactional attorneys and engineers works directly with law firm partners to build proprietary systems on top of Walton's existing transaction execution infrastructure. Each system is modeled on the firm's institutional knowledge and supports end-to-end transaction workflows, from term sheet negotiation and due diligence through document drafting, closing management, and ongoing compliance.

The Engagement Model

Every engagement begins by defining the scope of the system. Some firms focus on a single transaction type, such as venture financings or M&A; others build across several at once. We then determine the depth of the partnership, from working independently off the firm's precedent and transaction data to embedding alongside attorneys on live matters.

Whatever the approach, the objective is the same: scale the expertise of the firm's most senior attorneys across every transaction.

Anatomy of a Custom System

Consider a technology-focused M&A practice. After reviewing representative transactions and holding working sessions with the partners who lead them, we'll encode preferred positions and negotiation strategies. We'll build a terms map so every material term has a structured representation in the system. We'll develop an execution graph capturing the firm's preferred processes. We'll then integrate everything into Walton's core execution infrastructure.

The resulting system will autonomously execute representative M&A transactions under attorney supervision. It will draft the merger agreement and ancillary documents, coordinate diligence, model deal economics like purchase price adjustments and distribution waterfalls, maintain transaction checklists, and manage signatures.

The firm's institutional knowledge is embedded throughout. If a buyer proposes an indemnification cap below the firm's preferred threshold, the system identifies the issue, surfaces the firm's preferred position and approved fallback language, explains how the firm handled similar situations in prior transactions, and recommends a response.

Every system is fully white-labeled, with firm-specific data isolation and access controls. The firm's precedent, transaction data, playbooks, and encoded judgment remain isolated and are never used to train systems for other firms.

Why this Matters

Law firms adopted AI to gain a competitive advantage — first with general-purpose tools like ChatGPT, then with legal-specific platforms like Harvey and Legora. As those platforms scaled, they became shared infrastructure. Firms that adopted them to stand apart now run on the same systems as their competitors.

Meanwhile, attorneys are becoming far more sophisticated users of AI. They're building agents and developing their own internal tools. The limits of off-the-shelf platforms have never been more apparent.

The next phase of legal AI will be defined by custom systems that execute complex work and reflect the expertise, judgment, and competitive advantages of the firms that deploy them.

That is what Custom Transaction Systems are for.

Learn more about Custom Transaction Systems or contact our team below.