TENVIZ

Tenviz addresses the unstructured-data bottleneck in private-market investing. Allocators receive fund documents — LPAs, PPMs, quarterly reports — in PDF formats that require manual extraction of terms, exposures, and performance figures before any comparative analysis can begin. Tenviz's platform automates that extraction and normalization, producing structured datasets that feed directly into an allocator's existing portfolio-management and due-diligence workflows.

The firm's machine-learning models are trained specifically on the legal and financial language of alternative-investment documents — limited partnership agreements, capital call notices, and distribution statements — rather than general business correspondence. That domain-specific training allows the platform to recognize and extract the structured data fields that matter to allocators: management fee schedules, hurdle rates, waterfall structures, and portfolio-company valuation methodologies.

Tenviz's document-processing capability spans the major alternative asset classes that generate complex fund documentation — private equity, venture capital, real estate, and private credit. The platform is designed to handle the format variability across these strategies, normalizing output so that an allocator can compare a buyout fund side-by-side with a real-estate vehicle using consistent data fields.

Tenviz is fundamentally a workflow-software company, not a market-data vendor. It does not originate or sell investment data; rather, it provides the ingestion and structuring layer that allows allocators to extract and organize data from their own fund documents. The output belongs to the client, and the platform sits inside the client's existing technology and compliance perimeter.