When Abu Dhabi-based G42 announced a strategic investment in Inveniam in 2024, the firm presented the deal as a partnership focused on data provenance and artificial intelligence in private markets, according to G42. The investment was linked to work on the Saa’il Initiative, described as a derivatives marketplace for real-world assets based in Abu Dhabi.

Inveniam characterized its role in this context as a “data operating system” for tokenized private assets, signaling an ambition to sit at the core of new market infrastructure.

Within the following year, Inveniam moved from this strategic framing to concrete transactions. Public announcements and independent coverage describe acquisitions of Hedgehog Invest, Tractiv, Armada ETF Advisors, and Storj between early and late 2025.

Each deal added specific technical or regulatory capabilities that Inveniam did not previously control, indicating an attempt to build an end-to-end stack for tokenized private assets rather than a series of unrelated purchases.

The broader context for these moves is a growing effort to turn private-market data into standardized, machine-readable objects that can support tokenization and AI-driven analytics. Inveniam’s acquisitions target the main stages of that process, from document handling and storage to product wrapping and investor distribution.

The open question is whether the combined platform can function as a coherent system that is acceptable to institutional investors and regulators in multiple jurisdictions.

Executive Summary

  • G42’s 2024 investment provided capital and strategic backing for Inveniam’s build-out.
  • Acquisitions of Hedgehog, Tractiv, Armada ETF Advisors, and Storj map to distinct layers of an AI stack.
  • The combined stack is designed to carry private-asset data from documents into tokens and ETF structures.
  • Inveniam positions its role as a data operating system for tokenized private markets.
  • Key risks concern technical integration, regulatory differences, and AI data governance.

A Gulf Capital Inflection Point


G42 described its minority stake in Inveniam as a multi-year partnership focused on data provenance, AI model training, and private-asset markets, according to G42. The collaboration included the Saa’il Initiative, which has been presented as a derivatives marketplace for real-world assets to be developed out of Abu Dhabi.

This framing placed the relationship within a wider push by Gulf institutions to build local capacity in advanced data and compute infrastructure.

The regional strategy around AI infrastructure is visible in the plan by a G42 unit to build a 1-gigawatt AI campus in the United Arab Emirates, reported by The National. Locating a real-world-asset derivatives marketplace and a data-centric private-markets platform in this environment aligns with that emphasis on scale computing resources.

Inveniam’s data operating system thesis fits into a view that reliable datasets and verifiable document histories are prerequisites for monetizing AI investments in finance.

Anchoring development in Abu Dhabi also places G42 and Inveniam near local regulators and sovereign investors that are examining digital-asset infrastructure. For private assets that have historically been distributed through limited partnerships and bespoke contracts, the ability to test tokenization standards with supportive institutional capital is significant.

Company statements link the G42 capital to several years of technology development and acquisitions, which matches the sequence of deals announced in 2025.

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Why Fragmented Data Blocks AI


Private markets rely heavily on documents such as limited partnership agreements, loan contracts, appraisals, and operating statements. These materials are often stored as PDFs or spreadsheets, with key terms embedded in unstructured text.

That structure makes it difficult to run analytics at scale or to automate functions like risk monitoring and covenant tracking across portfolios of assets.

Tokenization, understood as representing asset interests on programmable ledgers, does not resolve these issues if underlying data remain incomplete or unverifiable. A 2025 report on tokenisation in payments and financial transactions by the Bank for International Settlements describes efficiency gains arising from standardized approaches and clear data models, according to the BIS.

Similar logic applies in private credit, real estate, and infrastructure, where investors and risk managers need consistent information about cash flows, collateral, and legal terms.

Inveniam’s platform is presented as addressing this gap by converting documents into machine-readable objects with embedded permissions and audit trails. Once documents are standardized and linked to verifiable sources, AI models can in principle support valuation, covenant surveillance, and scenario analysis.

However, each step from ingestion to storage and distribution requires specialized infrastructure, which Inveniam sought to assemble through its acquisition program.

The firm’s stated objective is to allow data about private assets to move through a controlled lifecycle, from original document to investment product. That lifecycle depends on traceable data lineage, secure storage, regulated product wrappers, and investor-facing channels, not only on core analytics.

The 2025 transactions can be read as an attempt to fill these specific roles around a central data operating system.

Hedgehog Invest: Channel to Investors


In April 2025, Inveniam acquired London-based Hedgehog Invest, which operates a white-label platform for issuing and trading real-world-asset tokens, as reported by Ledger Insights. Hedgehog’s software supports workflows such as asset onboarding, primary issuance, and secondary-market order matching for tokenized instruments.

The platform is designed to be offered under partner brands, which can integrate it into their own client portals.

By integrating Hedgehog, Inveniam extended its reach from internal data management into investor-facing tools. Instead of stopping at valuations or data rooms, the combined stack is intended to connect verified data objects directly to product creation and trading interfaces.

In practice, this could allow a private-credit pool or a real-estate portfolio whose documents have been standardized on Inveniam’s system to be issued as tokens through Hedgehog’s front end.

The acquisition also reflects developments in tokenized fund platforms more broadly. In 2025, WisdomTree expanded its institutional tokenized-fund platform across several blockchains, according to CoinDesk. Hedgehog gives Inveniam a way to offer comparable tokenization and trading functions without building a fully new investor portal and compliance stack.

Integration work between Hedgehog and Inveniam focuses on mapping token issuance fields to data-lineage tags so that each token can reference underlying documents. If executed as described, this would allow investors and service providers to trace a tokenized position back to specific contracts and supporting records.

For asset managers and wealth platforms, that level of linkage can support due diligence, reporting, and potentially secondary trading within known permission settings.

The strategic impact depends on whether clients adopt Hedgehog-based channels for new issuance or use them primarily as experimental add-ons. Family offices, wealth managers, and institutional investors may evaluate such platforms differently, based on internal compliance requirements and operational integration costs.

Inveniam’s bet is that direct control over a tokenization front end is necessary to connect its data operating system to real capital flows.

Tractiv: Proof of Provenance


Inveniam’s 2025 acquisition of Tractiv brought in a platform focused on secure data sharing and ledger-based file delivery, according to Inveniam. Tractiv assigns immutable hashes to each version of a document and records access events, creating a verifiable history of who viewed or modified which files.

This architecture is designed to support multi-party workflows without duplicating sensitive documents across multiple systems.

The approach echoes guidance on blockchain-based provenance in supply chains described in a 2022 study by the National Institute of Standards and Technology, which examined how distributed ledgers can support traceability of manufacturing data, according to NIST. In private credit and structured finance, similar traceability can be important because transactions may involve lenders, borrowers, servicers, rating agents, and custodians.

Each party needs assurance that documents have not been altered and that they are working from the same version.

In Inveniam’s architecture, Tractiv is being integrated into what the firm calls its digital middle office, according to Inveniam. This middle office is intended to host permissioned data rooms where participants can run analyses or workflows without extracting and emailing files.

For AI applications, such as models that estimate loan performance or property values, a verifiable record of input data is necessary to support model governance and audit.

Tractiv’s logs and access controls also relate to emerging expectations around explainable AI. If training sets, model outputs, and post-trade valuations can be linked back to specific document versions, it becomes easier to reconstruct how a particular price or risk score was generated.

This structure aligns with broader discussions about transparency in AI systems used for high-stakes decisions.

From an operational standpoint, the integration raises questions about how institutions will manage identity, permissions, and key management across multiple applications. Banks, asset managers, and service providers often have their own document repositories and access policies.

For Inveniam, the challenge is to make Tractiv’s provenance tools interoperable with existing systems while maintaining the integrity of its ledger records.

Armada ETF Advisors: Familiar Wrappers


Inveniam’s acquisition of Armada ETF Advisors in 2025 added regulated product design and distribution capabilities to the stack, according to Inveniam. Armada is described as a U.S. boutique focused on exchange-traded funds and interval funds linked to real estate and other less liquid strategies.

Interval funds allow investors to redeem shares at set intervals rather than daily, which can be suitable for portfolios that include private or hard-to-sell assets.

Regulated wrappers such as ETFs and interval funds are important because they translate new asset types into formats familiar to existing investors. A 2025 bulletin on the tokenisation of government bonds by the Bank for International Settlements discusses how tokenized instruments can improve settlement and transparency when combined with established legal frameworks, according to the BIS.

Similar logic may apply to funds that hold tokenized interests in private assets, where regulatory structures already define disclosure and governance standards.

By taking control of Armada rather than forming a loose partnership, Inveniam gains direct influence over how tokenized asset data are used in portfolio construction and product design. Armada’s investment teams can, in principle, draw on Inveniam’s asset-level datasets and Tractiv’s audit trails to construct strategies that reflect more granular information about income, covenants, or property characteristics.

Inveniam, in turn, gains a channel to seed funds and ETFs that can hold tokenized real estate, private credit, or other strategies.

This structure could provide an exit route for tokens issued through Hedgehog, if those tokens represent interests that can be held in regulated vehicles. For example, a pool of tokenized private-credit exposures might be aggregated into an interval fund whose shares trade on an exchange, while the underlying documentation remains in Inveniam’s data environment.

The feasibility of such structures depends on regulators accepting that tokenization and decentralized storage meet existing requirements for custody, valuation, and disclosure.

Armada’s role also highlights the competitive dimension of Inveniam’s strategy. Large asset managers and banks are building their own tokenization platforms, and some already offer tokenized money-market funds, as noted by a 2025 analysis of tokenised funds by the BIS.

Inveniam is attempting to differentiate itself by combining data provenance, AI tooling, and regulated wrappers under one corporate umbrella rather than relying solely on distribution relationships.

Storj: Distributed Resilience


In October 2025, Inveniam announced a definitive agreement to acquire decentralized storage provider Storj, according to reporting by CoinDesk. Storj operates a network that shards encrypted files across distributed nodes rather than storing them in a single data center.

The network uses cryptographic proofs to demonstrate that data remain available and correctly stored over time.

CoinDesk reported that Storj would operate as a subsidiary while providing core infrastructure for Inveniam’s analytics workflows. For asset owners and managers, a distributed storage model can be attractive when handling sensitive legal agreements, financial statements, and servicing data.

Spreading encrypted shards across multiple locations is intended to reduce single points of failure and to improve resilience against outages or localized disruptions.

Storj’s proof-of-storage mechanisms are also relevant to the question of data integrity in AI systems. If the same ledger that records Tractiv’s file hashes is linked to storage proofs from Storj, it becomes possible to construct an end-to-end chain of custody from document generation to analytical output.

Inveniam presents this linkage as central to its claim that model-generated prices and risk measures can be traced back to immutable source data, as of 2025.

Institutional acceptance of decentralized storage for regulated financial data is not yet settled. Some early uses pair Storj’s distributed network with more traditional backups in established cloud regions while regulators assess compliance implications, according to CoinDesk.

Supervisors may focus on issues such as data localization, resilience standards, and audit access when reviewing these models.

For Inveniam, control of Storj gives a direct handle on the storage and compute environment in which its AI agents operate. This can matter for both performance and governance, because the firm does not have to rely exclusively on third-party cloud providers.

It also aligns with the broader goal of situating a significant share of the private-markets data stack within infrastructure that can be coordinated with Gulf-based AI initiatives.

Can the Pieces Click?


With Hedgehog, Tractiv, Armada, and Storj in place, Inveniam’s roadmap now centers on integration. APIs for Hedgehog’s trading logic need to interoperate with Tractiv’s permission sets, Armada’s disclosure and portfolio workflows, and Storj’s key-management and storage proofs.

If these components do not align, data could become siloed, which would undermine the goal of a unified data operating system for private assets.

Regulatory considerations are layered across data, securities, and derivatives. Privacy rules differ across the United States, Europe, and the United Arab Emirates, which affects where sensitive documents and logs can be stored and processed.

At the same time, tokenized instruments may be classified under different securities or funds regimes depending on their structure, while derivatives on real-world assets such as those contemplated by the Saa’il Initiative add another set of regulatory reviews.

AI governance is a further axis of complexity. A 2023 paper on fairness in personalized advertising by Meta highlights the importance of transparent practices in model training and deployment, according to Meta.

Although the context is different, private-asset valuation and risk models face similar expectations that inputs and outputs can be examined and understood. Inveniam’s use of Tractiv and Storj is presented as a way to meet these expectations by linking model behavior to verifiable data histories.

Competition also shapes the outlook. Large incumbents are developing their own tokenization and data platforms, often combined with in-house AI teams and existing custody services. Crypto-native real-world-asset platforms are experimenting with on-chain lending, tokenized treasuries, and other structures, sometimes prioritizing open networks and composability.

Inveniam’s strategy of building a tightly integrated stack positions it differently, but success will depend on whether institutional clients prefer vertically integrated solutions or modular partnerships.

G42’s capital and strategic support have given Inveniam room to pursue acquisitions rather than rely solely on internal development or joint ventures. The next phase will test whether this approach produces a coherent operating environment for tokenized private assets.

Milestones to watch include live issuance of tokenized instruments that rely on the full stack, launch of funds or ETFs backed by tokenized private assets, and progress on real-world-asset derivatives under the Saa’il Initiative.

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