Meet Cyndy: The AI Brain Behind Cyndx’s Dealmaking Platform

Most software platforms have a learning curve. The better the platform, the steeper it tends to be. Cyndx has always been a capable tool for deal sourcing, M&A advisory, private equity research, and capital raising, but getting the most out of it meant knowing which tool to use, when to use it, and how to string them together. But that’s changing with Cyndy, the dealmaking platform’s new AI chatbot, embedded across our platform, letting bankers and advisors ask questions in plain language and get sophisticated, actionable answers in seconds, no menu navigation, no training session required.

The idea behind the chatbot is to provide the user with a sort of on-call analyst who already knows every corner of the platform, has read every data point, and never needs to be asked the same question twice.

Cyndy isn’t a standalone product bolted onto the side of the platform. It runs across several of Cyndx’s tools — Finder, Raiser, Acquirer, Scholar — and the underlying proprietary dataset of 33 million private and public companies. Ask it something as simple as “show me public comparables to Uber” or as specific as “find healthcare investors in California that typically invest in pre-revenue startups”, and it pulls the relevant results in seconds, drawing from Cyndx’s full data infrastructure rather than a single siloed source.

Sebastian Okser, Cyndx’s Chief Information Officer, puts it plainly. Most AI tools query one data source, which means their knowledge stops at the edges of whatever that source contains. “It’s like going to the library and reading one book,” Okser says. “You have all the knowledge of that one book, but you don’t know what happened in the rest of the library.” Cyndy draws from multiple datasets, reconciles discrepancies between them, and runs queries directly against Cyndx’s own databases, producing results that reflect a fuller picture of the market than any single vendor can offer.

How Does Cyndy Compare to General AI Tools?

General AI tools are powerful, but they’re built for broad use. They don’t understand the specific multi-step workflows that deal origination actually requires. Running a meaningful Finder search isn’t just typing a query. It means knowing which filters to apply, interpreting the results, refining the search, and knowing when to switch tools entirely. Cyndy understands those workflows because it was built specifically around them, by people who know how deals actually get done.

General tools can access an API (application programming interface) and pull basic information, but they don’t have a reconciled master dataset, and they don’t know the steps required to complete a deal sourcing task. Cyndy does. It uses routing algorithms to assess the complexity of each question and directs it to the right combination of tools accordingly, so a simple query gets a fast answer and a complex multi-step question gets the deeper analysis it needs.

There’s also a compliance angle worth noting. Using an unsanctioned AI tool to handle sensitive deal data creates what Okser calls “shadow IT”, a compliance risk no serious firm wants anywhere near a live deal process. Cyndy operates within Cyndx’s existing security framework, including SOC 2 Type II compliance, so the data stays where it belongs and is safe.

How Does Cyndy Actually Change How Bankers Work?

The most honest answer is that it eliminates the part of the job nobody wants to do. Onboarding takes time, and even experienced users don’t always know the fastest route to the answer they need. Cyndy removes that friction. Rather than learning when to use one tool versus another, or how to pull comparables alongside financial data, a user can ask the question and let Cyndy figure out the best path to the answer.

“Using Cyndx, you can solve in a couple of hours what used to take an analyst a couple of weeks,” Okser says. “Now it becomes a couple of minutes of refining.” That compression of time matters at every level of the organization, from managing directors who need a fast read on a market to associates pulling target lists together under deadline pressure.

Cyndy is also designed to be a starting point, not an endpoint. Rather than a truncated chat response, it links back to the full platform so users can work through structured rows and columns of data when the job demands it. Bankers think in spreadsheets. Cyndy respects that, and bridges the gap between a conversational interface and the structured data environment people actually work in.

What’s Next for Cyndy?

The roadmap points toward deeper integration throughout the platform, on company profile pages, search results, and across deal workflows, so the ability to ask a plain language question is available at every point where a user might need a fast answer, not just inside a dedicated chat window.

Running alongside Cyndy, Cyndx’s upgraded Scholar product now supports iterative, conversational research. Users can refine queries mid-process, adjust the contents of a deep research report without starting from scratch, and export finished work as a PDF, Word document, a summary, or PowerPoint deck, ready to hand to a client or an investment committee. A proprietary fact-checking layer runs a secondary verification process to catch and remove hallucinations before they reach the user, which is a meaningful commitment in a category where AI accuracy is still a legitimate concern.

Together with Cyndy and Scholar, the intelligence that surfaces a target, the research that builds the thesis, and the valuation that supports the deal can all come from one place.

To see what that looks like for your team, let’s talk.