Building Trustworthy, Scalable Document AI for Legal Tech

EvenUp Law

October 3, 2025

The lifeblood of many businesses is still locked away in documents: PDFs, images, tables, charts, handwritten notes, screenshots, and more.

In personal injury law, a single firm might manage a mountain of cases. Each case spans hundreds or even thousands of pages, collected over years. The answers that matter most—who did what, when, where, for how much, and backed by what evidence—aren’t neatly organized. Instead, they’re scattered across a chaotic landscape of formats and files, waiting to be discovered and connected.

That’s why Document AI, which uses LLMs and generative AI to transform unstructured documents into structured, actionable data, is becoming foundational infrastructure across legal, healthcare, insurance, finance, and the public sector. EvenUp’s work in legal tech is just one example of a larger movement: how to turn unstructured artifacts into durable knowledge, then automation, then decisions people can trust.

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The Three Responsibilities of Document AI

We view Document AI as a progression of capabilities, each building on the last to deliver increasing value:

1. Establish a System of Record (SoR)

First, transform unstructured documents into structured, persistent knowledge with clear provenance. Without this reliable memory layer, nothing else can scale or be trusted.

2. Build a System of Intelligence (SoI)

With a solid memory in place, the next step is automation. Drafting a demand letter draws directly from known facts, supplemented by retrieval for citations and relevant case knowledge. Human review remains available, but the default is efficient, autonomous operation.

3. Enable a System of Action (SoA)

Finally, enable timely, informed decisions. With a memory spanning cases, the system can proactively recommend actions—contacting providers, nudging stakeholders, or requesting missing records—at the right moment.

In this post, we focus on the importance and challenges of building a System of Record as the foundational step. In future posts, we’ll share our approaches to developing this layer and discuss how we extend these principles to the System of Intelligence and System of Action.

Why Early Document AI Stacks Hit a Wall

Most first-generation Document AI systems follow a simple pipeline: PDF → OCR → chunked retrieval (RAG). While this approach can produce impressive demos quickly, it soon encounters significant limitations:

• Fragmented context: Critical information is scattered across multiple pages, forms, and images. Chunking the document in a naive way means important connections are often missed, making accurate recall unreliable.
• No persistence: Answers are ephemeral—there’s no normalized memory or structured knowledge base that accumulates over time. The system doesn’t “remember” or improve with each interaction.
• Over-reliance on base models: As complexity grows, the system leans more on the LLM, leading to increased latency and cost, while consistency, determinism, and auditability decline.
• Lack of structure and lineage: There’s no schema to enforce, no lineage to trace, and no mechanism for human corrections to persist.

The Challenge of PDF Extraction: A Case for Reinforcement Learning

If a basic LLM wrapper isn’t enough, where do we begin? There are many challenges in building robust Document AI, but let’s start with the foundation: PDF extraction. This isn’t just a preliminary hurdle—it’s a microcosm of the entire problem space. Every complexity we’ll face downstream, such as unstructured layouts, ambiguous data, mixed modalities, and the need for reliable provenance, shows up right here in the first step.

The Invitation

Document AI is crossing a threshold—from clever wrappers to durable systems that remember, automate, and act with care. The research community is pushing multimodal reasoning, agentic evidence, and RL-based summarization. The industry is translating those ideas into platforms that can stand up to governance, latency, and cost. The prize is huge: an operating system that turns unstructured reality into operational memory—and then into outcomes people can trust.

We’re building along these lines and will share deeper dives on our System of Record patterns, evaluation and calibration loops, and multimodal training strategies. If this resonates, follow along—Document AI is a big tent, and the next breakthroughs will come from people who care both about ideas and about making them work in the world.