It's a question we hear frequently from private equity professionals evaluating AI tools for deal work. And it's a fair question. ChatGPT and Claude are remarkably powerful foundation models that have transformed how people interact with documents and data. These same models underlie ToltIQ's platform.
The short answer is while you can upload documents to general-purpose LLMs and ask questions about them, you're using tools that weren't designed for the precision, security requirements, and specialized workflows that private markets’ due diligence demands. ToltIQ bridges the gap between raw AI capability and the specific needs of PE deal teams working in virtual data rooms.
Here's what makes the difference.
General-purpose LLMs are broad-based foundation models designed to handle everything from creative writing to coding assistance. ToltIQ is purpose-built for document intelligence in VDR environments. Architectural distinction matters when you're analyzing CIMs, extracting financial statements, reviewing legal documents, and building cross-document intelligence, whether that means analyzing scanned or complex documents with precision, or querying hundreds to thousands of documents simultaneously across an entire data room.
Our purpose-built approach extends to how the platform manages context and memory. General-purpose LLMs weren't designed to treat entire VDRs as persistent knowledge bases. ToltIQ allows you to upload complete data rooms, run multiple analyses and sophisticated queries across the full dataset, and build permanent knowledge bases that grow with every transaction. You can reference past deals indefinitely and identify patterns across your portfolio. The platform maintains context across all related documents and builds comprehensive analytical threads throughout the diligence process, rather than forcing piecemeal analysis.
ToltIQ integrates directly into PE workflows and supports coordinated analysis across workstreams: financial analysts extracting metrics, legal reviewing contracts, and commercial assessing market position. All of this occurs while documents update, Q&A responses arrive, and findings must be synthesized into investment memos with clear source citations.
VDRs contain critical data across audited statements, management decks, customer contracts, and operating reports that must be extracted, normalized, and analyzed. ToltIQ handles structured financial analysis at scale and extracts this data cleanly,identifies cross-document anomalies like revenue discrepancies, covenant breaches, and margin compression patterns, and then delivers structured outputs that integrate directly into your models.
Teams collaborate in real-time within shared workspaces, analyze confidential documents in place without downloading them, maintain automatic version control as materials update, and generate findings with built-in citations linking back to source documents.
ToltIQ provides pre-written prompts tailored to PE workflows while supporting custom prompts for proprietary analysis. These prompts operate within an environment already optimized for private markets context, ensuring the system understands PE terminology, deal structures, and the insights that drive investment decisions.
The Prompt Library allows users to save and reuse prompts across deals, ensuring consistent, standardized output for their specific workflows. These prompts operate within an environment already optimized for a private markets context, ensuring that whether you're using our templates or writing your own, the system understands PE terminology, deal structures, and the insights that drive investment decisions.
The Bulk Query matrix feature extracts key terms simultaneously from thousands of documents, such as credit agreements, supplier contracts, and leases, in a single operation.
Our client team also provides expert service to users who would like their templates transformed into precisely-written prompts. Any due diligence checklist, IC memo, risk assessment, quarterly report, and other standardized documents can be reverse-engineered into a set of prompts that can be used repeatedly for new deals.
Our research team benchmarked ToltIQ against ChatGPT Enterprise on PE diligence tasks, which demonstrated measurable advantages:
These differences matter when your IC memos need to be built on comprehensive cross-document analysis that withstands partner scrutiny.
PE firms handle material non-public information across dozens of concurrent deals. Data isolation, audit trails, and zero-retention policies aren't optional. They’re fundamental requirements. A single security breach doesn't just compromise one transaction; it puts LP relationships, regulatory standing, and firm reputation at risk.
ToltIQ is SOC 2 Type II and GDPR compliant with security architecture purpose-built for financial services. This includes encrypted data at rest and in transit, network segmentation to isolate customer data, role-based access controls, MFA-enforced remote access, and contractual agreements with foundation model providers guaranteeing zero data retention. Your confidential deal information never trains underlying models. All activity generates audit trails linking outputs to source documents.
The platform's model-agnostic architecture provides additional flexibility as the AI landscape evolves. ToltIQ offers access to leading foundation models (Claude, ChatGPT, and Gemini) and allows you to optimize different tasks for different model strengths. When new models emerge, our research team tests them against PE-specific benchmarks before deployment. You benefit from the best available technology for each task without migrating platforms, re-training teams, or re-certifying compliance.
You can work with a general-purpose LLM for due diligence. Both ToltIQ and tools like ChatGPT or Claude let you upload documents and ask questions about them. The difference is everything built around that core capability.
General-purpose LLMs excel at broad conversational tasks, creative writing, and general research across diverse topics. ToltIQ channels that same underlying AI power through infrastructure specifically designed for the precision, security, and workflow integration that private markets’ due diligence demands. This includes VDR-scale architecture, persistent memory across thousands of documents, domain-optimized prompting, structured extraction at scale, seamless team collaboration, enterprise-grade data isolation, and access toexperts who help optimize your analytical workflows.
Want to see how ToltIQ handles your actual due diligence workflows? Request a demo at toltiq.com/contact and let's walk through a live example with your team.
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