The startup offers a data operating platform that enhances the liquidity of private market assets, specifically in private equity and commercial real estate, by ensuring data integrity and provenance. This enables asset owners, valuation firms, and investors to efficiently buy and sell assets while maintaining privacy and facilitating accurate price discovery.
Funding
$162.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Private market assets like real estate, private equity, and infrastructure suffer from a lack of transparency and standardization in data management, hindering efficient valuation, trading, and the creation of derivative financial products. The manual and fragmented nature of data collection and validation leads to inaccuracies, delays, and increased costs for asset owners, managers, and investors.
Solution
Inveniam provides a data operating system that brings transparency, trust, and efficiency to private market assets. The platform automates the credentialing, extraction, and structuring of data from various sources, creating a single source of truth for asset performance and valuation. By leveraging blockchain technology, Inveniam ensures data integrity, provenance, and auditability, enabling real-time access to trusted asset data. This allows asset owners to better manage and monetize their assets, while also facilitating price discovery, tokenization, and secondary trading for investors and service providers.
Target Audience
Inveniam targets owners and managers of private market assets, including real estate, private equity, infrastructure, and private credit, as well as valuation firms, investors, and service providers in these markets.
Features
- Blockchain anchoring for data credentialing and tamper-proof audit trails
- Federated data model allowing data owners to maintain control and provenance of their data
- AI-powered chat querying of document libraries for efficient information retrieval
- Robotic Process Automation (RPA) and scheduling tools to automate data management workflows
- Automated data field extraction from various file types using machine learning, AI, and NLP
- Virtual data rooms for secure and organized access to asset information
- Granular permission controls to manage data access for internal and external collaborators
- API integrations for seamless data exchange with other systems via JSON or Excel
- Tools to prepare enterprise data for AI tools, enabling queries across multiple data sources