Applied intelligence · banking

AXONQUAKE

A knowledge assistant for banking and financial institutions, built on a hybrid retrieval architecture: an enterprise RAG engine in Python paired with a high-performance graph-RAG engine in Rust, with citation tracking throughout.

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Architecture

Two retrieval engines, one assistant

AXONQUAKE combines two systems that are strong in different places, rather than forcing one approach to cover both.

Python

Enterprise RAG

Multi-stage retrieval built on RAPTOR hierarchical summarisation, hybrid dense-and-sparse search, and late-interaction reranking — tuned for precision over long, structured institutional document sets.

Rust

Graph-RAG

A high-performance graph engine over a property-graph store, with interactive knowledge graph visualisation — for questions where the answer lives in the relationships rather than in any single document.

Assistant

AXONQUAKE

One assistant, two engines — citation tracking carries every answer back to its source, whichever engine found it.

See it work illustrative

From documents to a cited answer

A scripted walkthrough of the shape of a real session — upload documents, watch both engines index them, then ask a question and watch the graph search happen before the answer arrives. Every document carries a classification; retrieval and citations respect it.

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Drop files, or add a sample set — PDF, DOCX, XLSX, CSV

Graph search

idle

Illustrative walkthrough — synthetic documents and a scripted query, not a live system. This is the shape of a session, not a benchmark.

Designed for

Regulated, multi-tenant, department-aware

Citation tracking — every answer carries its evidence back to source
Multi-tenant — isolated deployments per institution
Role-based access — department-level security boundaries
Domain grounding — configured to a bank's own regulatory and product context
Interactive graph exploration — analysts can follow relationships, not just read passages

Status

AXONQUAKE is in active development. Performance figures from internal benchmarking are available under NDA; we don't publish accuracy or latency claims on a marketing page ahead of independent measurement.