Walk into any investment bank or private equity shop today, and you’ll hear growing excitement about the potential of artificial intelligence for deep research reports. Yet dig deeper, and you’ll often discover that many firms are still relying on general-purpose tools like Microsoft Copilot for tasks that require specialized financial intelligence. The gap between AI’s promise and its current reality in dealmaking is wider than most would admit.

The truth is that Copilot wasn’t designed for the brutal realities of M&A research, and pretending otherwise may be costing dealmakers real money and missed opportunities.

Microsoft itself warns in its privacy FAQ that Copilot “may misrepresent the information it finds, and you may see responses that sound convincing but are incomplete, inaccurate, or inappropriate”. That’s hardly the confidence-inspiring message you want to hear when you’re conducting due diligence on a potential acquisition.

When users tried to make Copilot work for serious research, they got hit with disappointment. Microsoft’s own transparency note observed that the system is “fallible and probabilistic”, making it “challenging to comprehensively block all inappropriate content, leading to potential biases, stereotypes, or ungroundedness in AI-generated content”. In other words, it’s designed to be helpful across countless use cases, which means it’s not particularly excellent at any one specific task.

In a previous blog post, we asked Cyndx Scholar to generate a report on why it is more suited than ChatGPT for use by dealmakers. We ran a similar query, this time asking Scholar how Cyndx’s deep research tool outperforms Copilot. 

Where Copilot Falls Short for Dealmakers

The problems with using Copilot for dealsourcing go beyond Microsoft’s own disclaimers. Investment teams have discovered critical limitations that make it unsuitable for serious deal research:

  • No Access to Private Market Data: Copilot has zero access to proprietary financial databases, cap tables, or private market information. 
  • Citation Crisis: Ask for proper citations on critical data points, and you’ll get vague references that won’t pass muster in investment committees. 
  • Report Generation Limitations: Most worrying, Copilot can’t generate the comprehensive reports that investment professionals need. 

The content generation problems get worse from there. According to users, Copilot has file size limits that block larger documents, processing constraints that limit how much data it can handle, and a tendency to give responses that sound convincing but are actually wrong or incomplete. Try asking Copilot to generate a comprehensive 20-page investment memo analyzing a target company’s competitive position, and you’ll quickly hit these walls.

Purpose-Built Intelligence

While teams struggle with Copilot’s limitations, specialized GenAI platforms designed specifically for investment research are changing the game. Scholar represents this new breed of tools engineered from the ground up to solve the specific research challenges that create bottlenecks in deal teams. Here’s what makes it different:

  • Multi-Agent Deep Research: Unlike single-agent chatbots, Scholar orchestrates multiple AI agents in parallel to automate data collection, cross-reference findings, perform scenario analysis, and validate outputs in real time.
  • Professional-Grade Reports: Scholar automates the creation of comprehensive 20+ page research reports with detailed company profiles, logos, and custom branding, generated in just minutes.
  • Deal-Sourcing Intelligence: The tool integrates Cyndx’s deal-sourcing, capital raise, and acquisition-fit algorithms, delivering targeted recommendations and predictive analytics that guide users to relevant opportunities and risks.
  • Real-Time Validation and Citations: Scholar’s workflows conduct real-time validation of findings and provide proper in-text citations, significantly reducing the risks of AI hallucination and boosting credibility.
  • Data Flexibility: It can analyze not only its own structured datasets but also documents uploaded by users for contextualized, project-specific research.
  • Integrated Ecosystem: Scholar connects seamlessly with Cyndx’s other tools (Raiser for identifying investors, Acquirer for M&A fit analytics, and Finder for target discovery), making it a unified solution from research to execution.

The Integration Advantage

Our deep research tool doesn’t just access your existing files like Copilot. It combines your external files with our proprietary database of over 30 million public and private companies and trusted external sources, creating a research environment that understands both public and private market dynamics. 

But the real breakthrough isn’t just about better AI; it’s about integrated intelligence platforms that understand deal workflows. Copilot exists as a standalone productivity tool that requires you to verify information elsewhere, cross-reference multiple sources, and build your own analytical framework.

Purpose-built platforms take a fundamentally different approach. Scholar, for example, provides everything in one integrated environment: proprietary company data, external research capabilities, synthesis engines, and report generation with citations all working seamlessly together. You don’t need to jump between tools, verify information across different platforms, or worry about whether your sources are current and accurate.

In the time-pressured environment of M&A, while competitors are still piecing together information from various sources and double-checking generic AI outputs, teams using specialized platforms are already conducting scenario analysis, exploring complex market dynamics, and validating investment ideas.

Deep Research At Scale

Copilot helps you do the same work more efficiently, but it doesn’t fundamentally change what’s possible in your research process. Specialized platforms enable entirely different workflows. Teams aren’t just working faster, but exploring investment opportunities that would have been too time-intensive to research thoroughly before. They’re conducting more comprehensive due diligence because the tools make deep research feasible at scale.

We asked Scholar to analyze itself compared to Copilot for financial services research. The assessment: “Cyndx, as a high-growth vertical fintech, is setting the standard for AI-powered deal research and origination, leveraging its specialized knowledge graph and rapid report generation capabilities. In contrast, Microsoft’s Copilot ecosystem relies heavily on productivity integration, global reach, and compliance, addressing broad enterprise needs but lacking Cyndx’s tailored, proprietary research workflow.”

Yes, Copilot democratized basic AI assistance, meaning everyone has access to the same mediocre research capabilities. Using Copilot for deal research is like bringing a calculator to a supercomputer competition. Specialized platforms create genuine competitive advantages. When your research tools understand the nuances of financial analysis, market dynamics, and competitive intelligence, you’re uncovering insights and connections that generic tools miss entirely.

Transforming Financial Services

Private equity firms and investment banks using Cyndx’s industry-leading advanced tools and unsurpassed database are seeing transformation across their entire deal sourcing process. These firms identify opportunities earlier, conduct more thorough due diligence, and make faster decisions with greater confidence.

For private equity, this means superior deal sourcing and enhanced portfolio analysis. Investment banks benefit from deeper pitch preparation and the ability to provide clients with insights that create real value. The competitive advantage compounds as these firms consistently make better investment decisions.

Copilot showed everyone what AI assistance could look like, but for dealmakers who need precision and depth, general-purpose tools just can’t hack it. The future of GenAI deep research will be led by platforms that recognize serious dealmaking requires serious technology, not generic productivity assistants with an AI label. See how Scholar performs and supports financial services teams beyond generic output.