ToltIQ

• VDR-native processing for thousands of unstructured private-market deal documents
• Source-linked AI answers connecting findings directly to the underlying documents
• Bulk Query for extracting consistent, side-by-side answers across large document collections
• AI-powered workflows that organize repeatable due diligence sequences into reusable processes
• Blueprints that transform firm templates into reusable, automatically populated deal documents
• Vaults for storing shared research, regulatory documents, templates, and market data across deals
• Connected private-market intelligence through PitchBook, DealCloud, FRED, and SEC EDGAR
• Model-agnostic architecture supporting multiple leading AI model providers within one workspace
• Collaborative deal workspaces with configurable privacy, team chats, dashboards, and persistent deal context
• Enterprise security with single-tenant architecture, SOC 2 Type II, ISO/IEC 27001:2022, encryption, and zero data retention

ToltIQ Overview

ToltIQ is an AI-powered due diligence platform designed specifically for private markets investment professionals. It processes virtual data rooms and other complex deal materials, allowing investment teams to query documents, identify risks and opportunities, conduct financial analysis, and produce source-linked findings within a dedicated deal workspace. The platform is designed for private equity, private credit, diligence advisory firms, family offices, and other investment organizations working with large volumes of transaction information.

The company was originally known as DiligentIQ. It was founded by Ed Brandman, a former KKR partner and CIO, and developed a purpose-built generative AI platform for private-market due diligence. The company announced its name change to ToltIQ on June 11, 2025, while retaining the same ownership, leadership, and platform.

VDR-Native Processing

ToltIQ is built around the structure and challenges of virtual data rooms. Its platform can process large collections of unstructured documents with inconsistent formats, allowing deal teams to work across extensive transaction repositories. Findings are linked back to their source documents so users can verify the underlying information directly.

The platform also supports the ingestion of new documents into an existing deal environment. This allows the diligence workspace to maintain a current document set rather than requiring teams to repeatedly rebuild their analysis as additional transaction materials become available.

AI-Powered Due Diligence

ToltIQ provides AI-assisted analysis for financial, legal, technical, operational, and market information contained in deal materials. Users can ask questions across their documents and receive answers with source references, allowing findings to be reviewed against the original information.

The platform also provides pre-built prompts designed for private-market diligence and allows teams to create, save, and share their own proprietary prompts. This supports repeatable research processes across different deals while allowing investment teams to maintain their preferred analytical approaches.

Workflows

ToltIQ Workflows allow teams to structure recurring diligence activities into repeatable sequences. Instead of executing each research step independently, users can define a workflow, save it, and run the sequence when needed. This is intended to make recurring analysis more consistent across transactions.

The platform’s earlier Agent Mode adds an additional decision-making layer to query processing. It can refine a user’s request and determine an appropriate retrieval and analysis path before returning the resulting information.

Blueprints

ToltIQ Blueprints are designed to automate the creation of recurring deal documents. Firms can upload existing templates in formats such as PDF, Word, or PowerPoint, after which ToltIQ converts the structure into a reusable blueprint that can be populated with information from a deal.

This capability is intended for teams that repeatedly create investment documents using a consistent internal format. Instead of manually rebuilding the same structure for every transaction, the blueprint can provide a reusable framework for producing deal-specific outputs.

Bulk Query and Document Analysis

Bulk Query allows users to extract consistent answers from hundreds of documents in a single run. This is designed for questions where the same information needs to be identified across a large document collection and compared in a consistent format.

The platform can also analyze specific transaction materials such as confidential information memoranda, credit agreements, indentures, and other data-room documents. Its current mobile application description includes covenant and capital-structure analysis, CIM analysis, seller-bias flags, and cross-document checks.

External Data Connections

ToltIQ extends deal analysis beyond uploaded documents through connections to external data sources. Its current platform describes built-in connectivity to PitchBook, DealCloud, FRED, and SEC EDGAR, allowing investment teams to combine transaction materials with external private-market, financial, and regulatory information.

In June 2026, ToltIQ announced a partnership with PitchBook that made PitchBook’s private-markets dataset available as a Premium Connector inside ToltIQ. The integration is designed to let users compare deal materials with market benchmarks, comparable transactions, financing history, and sector information without leaving the platform.

Vaults and Shared Knowledge

Vaults provide a shared knowledge layer for investment teams. Firms can upload research, regulatory documents, templates, and market data once and use those materials as context across multiple deals. This allows recurring firm knowledge to remain available without requiring the same resources to be uploaded separately for every transaction.

Deal workspaces also support configurable privacy settings and team-visible chats, allowing organizations to control how information and conversations are shared across their investment teams.

Model-Agnostic Architecture

ToltIQ uses a model-agnostic architecture that can work with multiple leading AI model providers. The current platform identifies OpenAI, Anthropic, Google Gemini, and Cohere among its supported model providers. Users can select different models for particular tasks, while ToltIQ provides recommended defaults intended to balance speed and accuracy.

The company has described a structured internal evaluation process for assessing new models against real due diligence requirements. Its architecture is designed so model changes can be incorporated without requiring investment teams to rebuild their workflows around a new provider.

Security and Privacy

Security is a central component of ToltIQ’s enterprise platform. The company states that it has achieved SOC 2 Type II compliance with no exceptions and is certified to ISO/IEC 27001:2022. Its architecture uses single-tenant environments, TLS 1.2 or higher for data in transit, AES-256 encryption at rest, role-based access controls, continuous vulnerability scanning, and third-party penetration testing.

ToltIQ also states that it follows a Zero Data Retention policy and does not use client data to train AI models. Its published infrastructure includes U.S.-based data storage for U.S. clients and dedicated AWS infrastructure in Dublin for European clients.

Investment Team Workspaces

ToltIQ provides collaborative deal environments intended to keep the diligence process within one workspace. Teams can organize opportunities, access documents, conduct AI-assisted research, collaborate through shared workspaces, and monitor deal activity through dashboards.

The platform is also available through an iPad application for users with a valid ToltIQ enterprise account. The application supports document research, source-linked answers, deal analysis, templates, and deal-workspace functionality.

Pricing and Availability

ToltIQ does not publish a conventional per-seat subscription price. The company states that its pricing is usage-based and determined according to a firm’s deal volume over the course of a year. As a result, a smaller team with high transaction activity can have pricing comparable to a larger team with lower usage.

The platform is positioned as an enterprise product for private-market investment professionals rather than a general-purpose consumer AI application. Prospective customers can request a demonstration through the ToltIQ website, while the public site does not disclose a standard starting price.

Frequently Asked Questions

What is ToltIQ?

ToltIQ is an AI-powered due diligence platform designed for private equity, private credit, and other private-market investment professionals.

What does ToltIQ analyze?

ToltIQ analyzes virtual data-room documents, financial materials, filings, research, and other information used during private-market investment due diligence.

What is ToltIQ’s Bulk Query feature?

Bulk Query extracts consistent answers from large collections of documents in a single run, helping teams compare information across extensive deal materials.

What are ToltIQ Blueprints?

ToltIQ Blueprints convert existing firm documents into reusable templates that can automatically be populated with information from individual deals.

Does ToltIQ connect to external data sources?

Yes, ToltIQ provides connections to sources including PitchBook, DealCloud, FRED, and SEC EDGAR for research within deal workflows.

Is ToltIQ model-agnostic?

Yes, ToltIQ uses a model-agnostic architecture supporting multiple AI model providers and allowing different models to be selected for different tasks.

How does ToltIQ pricing work?

ToltIQ uses usage-based enterprise pricing determined primarily by a firm’s annual deal volume rather than charging a fixed price per user.

When did ToltIQ launch?

A specific public launch date for the ToltIQ brand was not disclosed; the platform previously operated as DiligentIQ before the ToltIQ name was introduced in June 2025.

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