VIRAM is not an AI system that must be trusted. It is an intelligence architecture that can be audited, verified, and held accountable — designed for organisations where decisions must withstand legal and regulatory scrutiny.
Most AI systems process documents on demand, forgetting everything between sessions. VIRAM is different.
Files are ingested once and stored permanently. No re-processing, no re-embedding, no recurring cost. Upload thousands of documents — they're available instantly, forever, until you delete them.
Query across 200 documents simultaneously. Find contradictions, trace themes, compare positions across your entire corpus — not one file at a time, but all at once.
The system accumulates knowledge. Every file, every interaction, every decision — remembered with perfect recall. No context window limits. No session resets. Your organisation's intelligence compounds over time.
When a minister asks how a conclusion was reached, when an FOI request demands the reasoning trail, when a Royal Commission examines the evidence — VIRAM provides answers, not excuses.
Every conclusion traces to source documents and reasoning steps. Every prompt-response pair is logged with timestamps. Users can retrieve any file or interaction in 2ms. Cryptographic hashing ensures tamper evidence.
742/742 claims verified against source content. Four-way classification: verbatim match, accurate paraphrase, severity softening (flagged), or fabrication (blocked). Hallucination is structurally prevented for document-grounded queries.
Outputs are formatted for formal review, not conversational appeal. Human-readable reasoning trails designed for non-technical oversight bodies. Explicit uncertainty and assumptions surfaced, never hidden.
95-99% of analysis happens before the model is invoked. The model does 1-5% of the work — constrained, verified, and correctable.
Documents are ingested once, stored permanently, and retrieved without model involvement. No token cost for ingestion. No model cost for retrieval. The processing pipeline is deterministic from ingestion through extraction, scoring, and verification.
Core logic is non-generative, inspectable, and repeatable. BRAIN performs semantic compression at ~95% fidelity, automatic batching beyond context limits, and cross-document analytical operations — all in code, not inference.
The model reasons from deterministically retrieved source content, not from training memory. It receives pre-structured analytical candidates, reducing token load by 70-80%. Models are interchangeable substrates — deploy your own, including air-gapped open source.
Every output is verified against source before delivery. Up to three education-and-retry cycles when verification fails. If correction fails, the system declares exactly where in the source document the user should verify — 100% success rate to date.
From document intelligence to autonomous research — both built on the same governable architecture.
Governable document intelligence
Infinite IQ — Autonomous research engine
Deploy on your servers, connect to your approved models, keep your data within your borders. VIRAM is designed for organisations that cannot compromise on sovereignty or auditability.
Whether you're exploring governable AI for your organisation or ready to deploy, we'd welcome the conversation.