Contaminated site portfolios accumulate decades of documentation representing an organization’s most complete record of site history. In practice, this knowledge is rarely accessible. Documents are fragmented across vendors, filing systems, and organizational memory. Evidence is rediscovered repeatedly as personnel and consultants cycle through. Site narratives drift. Decisions default to conservatism when evidence exists but cannot be efficiently located or trusted.

Artificial intelligence is increasingly proposed as a solution, but its indiscriminate application in regulatory and legal contexts introduces serious risks. Generative AI tools that “chat with documents” using probabilistic inference are fundamentally mismatched to the evidentiary standards that govern contaminated site work. Confident outputs without traceable sourcing have no place in environments where defensibility and audibility matter.

This presentation examines the principles behind a rigorous, documents-first approach to AI-assisted knowledge management for contaminated site portfolios. Drawing on practical implementation experience, we explore how historical document sets can be systematically converted into governed, evidence-cited knowledge bases supporting natural language navigation while remaining fully constrained to indexed source material. We discuss governance frameworks, traceability requirements, and workflow design decisions that separate a durable corporate memory system from a novelty chatbot.

Attendees will leave with a clearer understanding of what it means to build institutional knowledge that outlasts individual projects and personnel.