Inveniam, the data infrastructure company that anchors more than $200 billion in private market assets across the chain, is on track to close an industry gap with its latest partnership with Docugami.
Both parties have come together to bring verifiable element-level document data to real-world assets.
Like most industries, artificial intelligence has made its way into private markets; However, the data challenge also came with it, because the data underlying the analyzed assets is sometimes stuck in unstructured documents that machines have never been able to read with precision.
The deal will bring into action both Docugami’s Document Graph Markup Language (DGML) and Inveniam’s NVNM Chain.
The collaboration agreement followed earlier reports in which Inveniam announced its intention to make an acquisition $MANTRAthe regulated Layer 1 blockchain on which NVNM Chain is built as a Layer 2, in a transaction expected to close at the end of this month.
That deal followed a $20 million strategic investment in which Inveniam made $MANTRA in August 2025, and the two companies built NVNM Chain together before Inveniam moved to bring the entire stack under one roof.
What is Docugami’s proprietary technology?
DGML was created by Jean Paoli, CEO of Docugami, co-author of the XML 1.0 standard and former president of Microsoft Open Technologies.
It converts leases, loan agreements, operating statements and valuation reports into accurately labeled data elements.
Inveniam’s NVNM Chain then records these elements as tamper-resistant, time-stamped artifacts on the chain, creating a cryptographic audit trail that any authorized counterparty can independently verify.
DGML is different from other document data technologies
Currently, there is already a technology to determine the source of a document as a whole, proving, for example, that a specific rental list existed at a certain time.
DGML takes that innovation to the next level, as it makes it possible to verify individual data points from a document. For example, a rental amount from a specific lease clause, a loan-to-value ratio from an underwriting memo, or a net operating income line from an operating statement.
“The world’s most important business decisions are made on the basis of documents that machines have never been able to read properly,” said Paoli, speaking about the Inveniam scheme.
He said: “We have spent years building the technology to turn complex documents into data with unsurpassed precision. By opening DGML, we invite every participant in the private capital ecosystem to partner with us and build on a shared foundation.”
Docugami’s technology uses open-source large language models, small agentic reasoning models, and knowledge graph generation to transform complex business documents into structured, actionable data without the need for training data or templates.
Why do tokenized assets and AI agents need to anchor document data into the chain?
The tokenized RWA value has reportedly grown from approximately $14.1 billion in January to more than $32.4 billion. However, the data quality issue still remains an issue because tokenizing or attesting a private asset is only as reliable as the underlying data.
Verifying the source of that data required either trusting the issuer or conducting independent due diligence.
NVNM Chain, which Inveniam launched on May 7 as the purpose-built Layer 2 for private markets, is designed to serve as an attestation layer for agentic AI.
There is a NVNM Chain Records dataset that provides Proof of Origin, Proof of Condition and Proof of Process certificates that track what data drove a decision or transaction.
With DGML in the mix, AI agents can read the document data and make it both verifiable and audit-ready.
According to Patrick O’Meara, chairman and CEO of Inveniam, “DGML is a fundamental advance in the way we read and structure the documents that drive private capital.”
He stated: “Embedding data elements extracted by DGML into the chain is the natural complement: it ensures that the data elements, once surfaced, can be trusted by any stakeholder who needs to use them.”
The two companies have not yet announced a date when their solution will be publicly available.