VIRAM checks every claim the model makes about your documents against your documents — and tells you, in the answer, where it got something wrong. You get the analysis and a list of what could not be verified. Nothing is quietly dropped. Nothing is quietly wrong.
Most AI systems re-read your documents on every question and forget everything in between. VIRAM reads them once.
Files are ingested once. File name, structure, section, paragraph and page are written onto every chunk at that moment and never recomputed. No re-processing, no re-embedding, no recurring ingestion cost — the corpus is prepared, permanently.
Ask one question across your entire corpus. There is no fixed document limit: work is batched, so the constraint is how long you are willing to wait, not the model's context window. Find contradictions, trace themes, compare positions across everything at once.
Your documents live in a separate store, not in the model's context and not in its training. The model never has to remember anything. Attach files or query from memory — the answer is built from the same content either way, and the store outlives any model you choose to run.
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 carries the file, the structural location and the page it came from. Every prompt and response is logged with a timestamp. Any cited passage can be retrieved and read in 1–2ms. Entries are chained with SHA-256 — each one hashes the one before it, so a modified record breaks the chain.
Every claim attributed to a source document is checked against that document before you see it. Quotes are matched against the text. Cited locations must exist and must contain what the claim says they contain. Where a wrong citation can be corrected, it is. Where it cannot, the system says so and names the place in the source for you to check.
The verification layer sits above the model, not inside it — so it reports on whichever model you run. When output is softened, misattributed or invented, the notice names the failure and identifies the model as the cause. You are not asked to trust the model. You are shown where it fell short.
Around 90% of the work happens before the model is invoked — in deterministic code you can inspect, log and repeat. The proportion varies by query.
Documents are ingested once, stored permanently and retrieved without involving a model. Extraction, structural labelling, chunking, retrieval and verification are code, not inference. Retrieving a stored fact costs nothing and returns the same result every time.
BRAIN assembles what the model receives: structure, location and content drawn from across the whole corpus. Where content exceeds what a single call can carry, it batches and synthesises across batches rather than sampling. It reads every part of every document — not the opening, a little of the middle and the end, which is what a long context window does in practice.
The model reasons from retrieved source content, not from training memory, and receives pre-structured material rather than raw text. Models are interchangeable: run an open-source model on your own hardware, or your organisation's approved API. Nothing in the architecture depends on which one you choose.
Output is verified against source before delivery, with up to three correction cycles. What cannot be verified is listed for you, not removed. And if answering your question properly would exceed the model's context window, VIRAM does not return a partial answer — it states the size of the request against the limit, and stops. A confident half-answer is the most dangerous output an AI system can produce.
Document intelligence over your own corpus, and research that brings vetted external science into it.
Governable document intelligence
Infinite IQ — research acquisition and synthesis · in development
Deploy on your servers, run the model you have approved, keep your data inside your borders. Core is designed for organisations that cannot compromise on sovereignty or auditability.
VIRAM is in active development and we are talking to organisations interested in early deployment. If your decisions have to withstand scrutiny, we would welcome the conversation.