How does Mercurium reduce onboarding time?

Mercurium automates document classification, KYC/AML checks, entity mapping, and risk scoring. What traditionally takes 90 days is reduced to minutes through AI-driven parallel processing.

Is Mercurium deployable on-premise?

Yes. Mercurium supports private GPU, on-premise, and enterprise deployments with full data sovereignty and custom model retraining on client data.

What document accuracy does Mercurium achieve?

Mercurium achieves 99% layout recognition accuracy and 97.6% table extraction accuracy across diverse document formats and languages.

Articles & Research

Things we write about,
things we open-source, things we think are true.

Product deep-dives, compliance research, open-source tooling, and our take on where this industry should be headed. Written by the people building Mercurium.

Everything Articles Research papers GitHub

Open Source

Open-source modules we ship

We're building multiple open-source modules and MCP servers around financial transparency — full-text + vector search, entity disambiguation, document structure extraction, and more. Star, fork, contribute — or wire them into your own stack.

pg-search-vector

Mercurium-Analytics/pg-search-vector · GitHub

Drop it into any Django app to get sub-100ms semantic search across millions of rows. Combines keyword BM25 + meaning-based vector embeddings — finds the right entity even when names are misspelled or transliterated.

Python PostgreSQL pgvector Django

Things we write about, things we open-source, things we think are true.

Open-source modules we ship

Things we write about,
things we open-source, things we think are true.