American startup DiligenceSquared has introduced a platform that uses artificial intelligence to conduct part of the due diligence (comprehensive review) for mergers and acquisitions (M&A) deals. The key innovation is voice AI agents capable of autonomously conducting structured interviews with the customers or partners of a company slated for acquisition. This allows for the collection and analysis of qualitative data on reputation, customer satisfaction, and synergy without involving traditional consulting firms, whose services can cost hundreds of thousands of dollars per project.

Due diligence is a mandatory but extremely resource-intensive stage of any major deal. An investor needs to deeply understand the business, its risks, and potential. Traditionally, to gather qualitative data (such as customer feedback), consultants are hired to manually conduct dozens or hundreds of interviews. This process takes weeks, costs a fortune, and is therefore often inaccessible to mid-sized and small investment funds, limiting their opportunities. DiligenceSquared specifically targets this problem, democratizing access to deep qualitative analytics.

The startup's technology operates on a clear algorithm. First, the system analyzes the target company's business model and the due diligence objectives to create a personalized questionnaire. Then, AI agents, using speech synthesis and natural language recognition, call or conduct online conversations with a pre-agreed list of respondents (customers). The agents can ask follow-up questions, adapt the dialogue, and record not only verbal answers but also intonations. The collected data is automatically transcribed, analyzed using NLP models to identify themes, sentiment, and risks, and compiled into analytical reports with key insights.

While there hasn't been much public reaction from major M&A market players to the product yet, the startup has already attracted the attention of several venture capital funds and, according to some reports, has begun pilot projects with several mid-level investment companies. Industry experts note that such automation was only a matter of time. "Manual collection of qualitative data is a black box that significantly slows down deals. If AI can provide comparable quality of insights at a radically lower price, it will change the game for many funds," an anonymous partner at a venture firm is quoted as saying in one industry publication.

The adoption of such technologies signifies a major shift for the financial and strategic consulting industry. On one hand, it lowers barriers for smaller players, allowing them to compete for deals with deeper analytics. On the other hand, it creates pressure on traditional consulting firms, forcing them to either drastically reduce prices or integrate similar AI tools into their processes. For end-users—fund managers and corporate development teams—this means faster deal execution, lower transaction costs, and the ability to conduct due diligence more frequently and at earlier stages of interest in a company.

The development prospects for this direction are clear: further increasing the "intelligence" of agents to handle complex dialogues, integration with financial data analysis platforms, and expansion of application areas. Open questions remain concerning ethics (whether respondents are informed they are speaking with an AI), the depth of analysis compared to an experienced consultant, and the cybersecurity of confidential data obtained in the process. The success of DiligenceSquared will likely trigger a wave of similar solutions, which could ultimately make deep due diligence a standard service even for smaller deals.