What makes this different:
Air-gapped: Zero cloud dependencies. Uses local LLMs via LM Studio (Qwen, etc.)
ACH Methodology: Implements the CIA's "Analysis of Competing Hypotheses" technique which forces you to look for evidence that disproves your theories instead of confirming them
Corpus Integration: Import evidence directly from your documents with source links
Sensitivity Analysis: Shows which evidence is critical, so if it's wrong, would your conclusion change?
The ACH feature just dropped with an 8-step guided workflow, AI assistance at every stage, and PDF/Markdown/JSON export with AI disclosure flags. It's better than what any given 3-lettered agency uses.
Tech stack: Python/Reflex (React frontend), PostgreSQL, Qdrant (vectors), Redis (job queue), PaddleOCR, Spacy NER, BGE-M3 embeddings.
All MIT licensed. Happy to answer questions about the methodology or implementation! Intelligence for anyone.
Links: Repo https://github.com/mantisfury/ArkhamMirror
ACH guide with screenshots at https://github.com/mantisfury/ArkhamMirror/blob/reflex-dev/d...
I'm loving the approach you took to the UI! I had some similar ideas in mind and plan to build narrative reconstruction and timeline view tools too so it's really nice to see how others have done so! I'll definitely be following your work and I shared your project in the OSINTBuddy discord to hopefully get some more eyes on it :)
Great work, I hope you keep at it :)
This is super interesting. I will probably (hopefully?) never need to use it, but interesting nonetheless. It also makes sense to have this type of application airgapped. Journalists need to have near-perfect OPSEC depending on what they are working on.
I do think though that this approach will become annoying quick:
https://github.com/mantisfury/ArkhamMirror/blob/main/scripts...
I really would like to know how good this would be for a corporate Internal Audit workflow/professional.