From Documents to Decisions: Unlocking Trustworthy Data Retrieval in High-Stakes Legal AI workflows

As law firms face growing pressure to deliver faster, more consistent outcomes without relying solely on billable hours, many are turning to AI to unlock scale. But in legal practice, success is not determined by model choice alone. It hinges on how deep legal knowledge of contracts, correspondence, chronology, and expert judgement, can be extracted, structured, and trusted.

In this session, we share lessons from building and evaluating AI-powered retrieval systems over large, highly nuanced legal document corpora. Working in a domain where documents are long, inconsistently structured, and rich with implicit relationships, we encountered challenges that will be familiar across many regulated industries: ambiguous language, evolving terminology, fragmented evidence, and severe consequences when retrieval goes wrong.

Rather than starting with multi-agent orchestration, we discovered that the greatest gains came from rethinking the data layer. We present a set of practical experiments comparing three approaches to retrieving legal knowledge: traditional retrieval-augmented generation, knowledge-graph-enhanced retrieval that explicitly models entities and relationships, and agentic search patterns that decompose complex legal questions into intent-driven retrieval steps.

A key insight was the role of evaluation as a design tool. We show how we built an evaluation framework that mirrored real legal workflows, combining LLM quality metrics, retrieval diagnostics, safety checks, and domain-specific simulations. This allowed us to systematically improve accuracy, traceability, and trust which critical factors in a high-stakes legal setting.
Although rooted in the legal domain, the patterns we share are broadly applicable to any organisation working with complex, document-heavy data. Attendees will leave with practical insights into how thoughtful data extraction, modelling, and evaluation can transform AI from a generic assistant into a dependable, domain-aware capability.

• How to design and compare RAG, knowledge-graph, and agentic retrieval approaches for complex, high-stakes documents
• How to use evaluation as a design tool to improve accuracy, traceability, and trust in AI systems
• Practical patterns for extracting and structuring domain knowledge to build dependable, domain-aware AI