AI systems make claims. Elvane Ledger preserves the evidence behind them, so people can review the original sources later and detect changes to the recorded evidence. Unchanged evidence does not establish that a claim is true.
A local evidence and provenance library for AI-generated work. Elvane Ledger records the sources, exact passages and claims behind a report, so a later reviewer can inspect them and check that they have not changed.
Keep important AI work reviewable across models, tools, and changing workflows.
Give reviewers a durable path back to the context behind important outputs.
Make high-value AI work easier to audit, reproduce, and independently check.
Explore three scripted claims and their preserved sources. Check that the files are unchanged, then see how a disposable edit is detected. Evidence integrity does not establish claim truth.
Customer concentration is 41%.
In this recorded example, a fresh reviewer recovered all three claims and their exact source passages from the exported bundle alone, without the original producer store. The demo shows that recorded result; your browser checks file integrity and does not run reconstruction.
A renewal guarantee is recorded with an inadequate excerpt. Its unsupported label is an author annotation. Neither intact bytes nor successful reconstruction establishes factual support.
Prerecorded Ledger operations and scripted model output. No live AI.
A confident answer is still a set of claims. Reviewers need the source passages behind those claims and a way to check whether the recorded evidence has changed.
AI systems can produce useful results quickly, but organizations still need trustworthy records of the context behind important outputs.
As AI systems interact with tools and business processes, teams need clearer records of what occurred and how to review it.
Reviewers should be able to examine the supporting record instead of relying only on a generated summary or confidence score.
Elvane is being designed so teams can distinguish recorded evidence from downstream conclusions and verification outcomes.
Keep the evidence needed for later independent review, even after the original workflow has ended or the tools have changed.
Elvane is not another model or agent framework. The product is intended to complement existing systems by adding durable evidence and provenance around important AI work.
Ledger adds an evidence record around important AI work. It does not replace your models, agents or workflows, and it is not tied to a single model provider. Today Ledger runs as a local library: evidence is stored as files in a location you choose. It is not a hosted service, and any hosting or data-handling arrangement for a pilot would be discussed with you, not assumed.
Ledger runs today as a local library. It records claims, source passages and their links, with integrity checks that detect changes to the recorded evidence.
We’re looking for teams building AI workflows where the evidence behind generated outputs needs to remain reviewable.
Tell us about the AI evidence you need to preserve and review. We can discuss whether Ledger is a fit in a private conversation.
Start with a conversation about your workflow.
Vajraya Labs is an early-stage AI infrastructure company incorporated in 2026. The company develops Elvane, a product family for evidence, provenance, verification, and review of AI-generated work.
Teams that produce AI-generated reports, research or analysis that will need to be reviewed later, where it matters which sources and passages each claim relied on.
Elvane Ledger is the first product: an evidence layer that complements existing AI stacks rather than requiring organizations to replace their models or workflows.
Elvane Ledger is an early, local library. Current work is focused on validating it on real report-review workflows with a small number of design partners.
Vajraya Labs is looking for teams with AI-assisted research or review workflows who want to evaluate Ledger on one real workflow.
Our mission is to build technology that increases human understanding by preserving the evidence behind intelligent systems.