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Home › AI Use-Case Library › Grounded knowledge assistants: 6 real deployments
Assistants answering from a specific corpus: medical references, case law, lecture archives, company files.
Retrieval-grounded systems rarely get described as such in the press, so this list is assembled from deployments where the assistant demonstrably answers from a defined body of material rather than general training. Expect it to grow as technical sources are added.
deployed tender-writing agent using five structural mechanisms — machine-verified verbatim quotes with page citations, coverage accountability, gated drafting, capability auditing routing to partner slots, and explicit bracketed unknowns — to refuse fabrication rather than confabulate
Reported: on a £950,000, 133-page tender the system pulled 45 mandatory requirements and flagged 11 as unanswerable in a 5-minute draft
Reddit uses an LLM-powered Rules Hub to judge whether posts and comments match the intent of each community's rules.
Reported: The tool was piloted with more than 700 subreddits and is now available to all new communities, with a full rollout planned later in 2026.
Deployed proprietary Thomson LLM trained on Westlaw, Practical Law, Checkpoint and Reuters content inside CoCounsel Legal's Tabular Analysis feature for law firms and corporate legal departments
Training Grok AI on the sum total of SpaceX's internal information and employee contributions
Mount Sinai integrated the OpenEvidence LLM clinical reference enterprise-wide inside its Epic EHR for physicians, nurses and pharmacists.
Arizona State University quietly deployed Atom, an AI platform built on Anthropic's Claude that ingests faculty lecture videos, slides, and assignments to generate $5/month learning modules, without faculty knowledge.
Every entry names the organisation and links its source. Outcome figures are quoted as reported, never estimated. Vendor announcements without a named customer are excluded. Halted and reversed deployments are kept on purpose.