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CLUSTER ANSWERS-DOCUMENTS-KNOWLEDGE · 24 ENTRIES · EARLIEST ENTRY 2026-09-16

Enterprise document Q&A with citations

This cluster covers enterprise knowledge platforms that ingest documents and answer questions with cited sources via chat and retrieval-augmented generation. The dated history is too thin to read a shape from, with only 3 entries predating the index start, so the cluster likely predates the index or many members are undated. Traction leaders are DocVectra (8 stars), ResearchAI-MultiAgent (3 stars), and langchainrag (2 stars).

Entry timeline

ENTRY TIMELINE

MAY 2026: 1AUG 2026: 2SEP 2026: 15OCT 2026: 6
MAY 2026OCT 2026

Each bar is the month a member carries, which is a creation date where the source published one and a crawl date otherwise. Nothing in the index was found before 11 August 2026, and coverage broadened sharply on 14–15 August, so a bar from then on reports how much we found that month, not how much was built; an earlier bar reports how far back a member's own source dated it — not how long we have been watching.

Members by activity

Activity signals, not revenue.

Build origin · 4 unlabelled

Operator · 2 hobby · 2 unlabelled

Two labels per entry, never collapsed into one: how it was built, and who stands behind it. Counted over the 4 listed below. Unlabelled means nothing has decided yet — not that the answer is small.

  1. 21material-research0★1 mo—Self-hosted research tool that ingests paper, code,and dataset archives; indexes them into a citable knowledge base and answers questions with cited page-level sources.
  2. 22rag_project0★1 mo—hobbyChat dashboard that answers user questions from ingested documents with source citations and document statistics.
  3. 23podcast-ai-knowledge0★1 molivehobbyAI knowledge assistant that indexes uploaded podcast transcripts and long documents, then answers questions with cited sources in a streaming chat interface.
  4. 24enterprise-rag0★26 days—Enterprise knowledge base that ingests documents and answers questions with cited sources.

Ranked by traction, highest first, and paged 20 at a time — this is a window on the cluster, never the whole of it. Ties are broken by a stable id so a product cannot appear on two pages or fall between them. Browse every cluster.