Why ChatGPT is not the right tool for M&A longlists

A lot of deal teams are experimenting with general-purpose AI tools like ChatGPT, Claude, or Grok to speed up longlist creation. The idea makes sense on the surface: longlist research is time-intensive, and LLMs are fast. But the results tend to disappoint, and the reasons are structural, not fixable with a better prompt.

Here is what the problem actually is, and what a purpose-built approach looks like.

No access to the data that matters in M&A

M&A analysis depends on verified, structured data: company registry entries, balance sheets, ownership structures, transaction histories. General-purpose LLMs do not have access to any of this. They pull from surface-level web content, which means their outputs are heavily shaped by which companies have good SEO and active marketing, not by which companies are actually relevant to a transaction.

Concretely, what is missing:

• Registry data: events like capital increases, management changes, or insolvency filings are not captured
• Financial KPIs: revenue, EBIT, and headcount cannot be reliably filtered
• Ownership data: shareholder structure, owner age, institutional involvement, and succession indicators remain invisible

These are precisely the signals that matter most when identifying targets or buyers in the mid-market.

Hidden gems stay hidden

Some of the most attractive M&A targets are mid-market companies with little or no online presence. No SEO strategy, sometimes no website at all. They are profitable, often hold strong niche positions, and are exactly the kind of company that does not show up when an LLM queries the web.

General-purpose tools systematically surface the companies that are already well-known and visible. That creates blind spots on the buy side and misses relevant buyers on the sell side, particularly in the small-cap segment where the most interesting counterparties are often the least prominent ones.

Hallucinations are a real risk in M&A work

LLMs fabricate. Company names, revenue figures, ownership details, sector classifications: all of these can be generated with complete confidence and be entirely wrong. In a deal process where factual accuracy is non-negotiable and conclusions get presented to investment committees, that is not an acceptable risk to manage after the fact.

Any tool used in M&A longlist work needs to pull from verified sources and flag gaps explicitly, not fill them in with plausible-sounding fiction.

Filters need to be deterministic, not approximate

A professional longlist requires hard filters: revenue above a certain threshold, employee count within a defined range, specific ownership types, geographic focus, transaction history. LLMs treat these criteria as qualitative suggestions embedded in a prompt, not as deterministic rules. The result is a list that roughly reflects the intent but cannot be relied upon as a clean, filtered dataset.

For buy-side or sell-side work, that lack of precision is a problem at every downstream step.

Data privacy cannot be an afterthought

Uploading deal-sensitive information into a generic AI tool raises real compliance questions. Where is the data processed? Is it used for model training? Who has access? For professionals handling confidential mandate data, these are not hypothetical concerns.

Specialised tools built for the M&A context operate on European servers, apply strict data separation, and provide clear documentation on how data is handled. That is the baseline for anything used in a regulated professional environment.

What a purpose-built approach looks like

StrategyBridgeAI is built specifically for the analytical demands of M&A, corporate finance, and banking. The platform provides access to around 50 million private and public companies across more than 100 countries, combining financials, ownership structures, qualitative company data, transactions, and contact information in a single workflow.

Search works via a semantic chat interface: describe the business model, market logic, size, or ownership type you are looking for, and the platform finds matching companies, including niche players and private companies that never appear in traditional database filters. Hard filter logic sits alongside the semantic search, allowing teams to screen by financial KPIs, ownership type, sector, geography, and transaction history. Results export directly to Excel with all relevant fields included.

The difference shows up quickly in practice.

"Longlisting is where we get the most value. We now work systematically rather than subjectively, and significantly faster."
Dr. Dirk Pramann, Mition Mittelstandsbeteiligungen

"For longlists, we used to spend two or three weeks. Now we get it done in two or three afternoons. For multiple valuations, we are at 85 to 90 percent time savings."
Nikolai Üstündag, WTS Advisory

Read more about how clients use the platform: WTS Advisory client story and Mition client story.

Request 30 potential targets for your search field or book a demo to see the platform in practice.