arcAIngel is a forensic-intelligence platform engineered to map, analyse,

and expose systemic malfeasance across institutional networks. Integrating

heterogeneous data—contracts, communications, financial flows, and public

records—it builds a living "graph of accountability" and delivers

court-admissible evidence packages to regulators, litigators, and oversight bodies.

Graph Neural Network backbone for cross-entity relationship learning

Retrieval-Augmented Generation (RAG) for contextual evidence synthesis

Cryptographic provenance (hash-chain, optional blockchain anchoring)

Multi-modal ingest: PDF/OCR, email, audio transcripts, structured DB

Evolving-graph embeddings that track entity behaviour over time

Detects when actors intensify collusion or exit a fraud ring

Highlights dormant connections that reactivate near key financial events

Goes beyond correlation to identify probable cause–effect chains

e.g., "Funding cut X preceded service failure Y in 87 % of cases"

Crucial for tribunals requiring evidence of proximate causation

Custom classifier trained on legal precedent corpora

(Contract law, fiduciary standards, public-authority obligations)

Outputs ranked duty-breach probabilities with explainable rationale

Detects signs of evidence spoliation or coordinated deletion

Alerts analysts when adversaries may be covering tracks

Logs all external access attempts for litigation-hold scenarios

Optional deployment in TEE (Trusted Execution Environment)

for ultra-sensitive investigations involving national-security data

or journalistic source protection

RESTful + GraphQL APIs for integration with case-management,

e-discovery platforms (Relativity, Nuix), and regulatory portals

One-click submission packs formatted to FCA, ICO, SFO templates

Real-time stream ingestion for live transaction monitoring

Federated learning across allied investigative bodies

(privacy-preserving model updates without raw-data sharing)

Augmented-reality "evidence room" for immersive case review