Introducing Twing.AI

Why data and workflows, not language models, are the real unlock for PE

If you’re a private equity buyout fund in the lower to middle market, your deal team might review hundreds of deals a year, only to close a handful. But how many hours is your team burning on deals that go nowhere? And when you pass, what happens to all the institutional knowledge gained in the process?

There’s future investment edge buried in those passed deals. Patterns you noticed, red flags you caught, comps you pulled. But where does that knowledge live? In the head of the VP running the deal. Stuck on desktops. Buried in subfolders. With the associate who left two years ago. With the partner who’s looked at so many deals over 20 years that nobody even knows what to ask him.

The next associate who works on a similar opportunity starts from scratch. Your firm retraces the same steps, instead of turning compounding knowledge into a competitive edge.

That’s what we built Twing.AI to fix. Twing.AI is a managed service AI and data partner embedded across the full private equity deal lifecycle.

Who we are

We’re a team of seasoned experts across data and finance that puts data quality, integrity, and structure at the core of what we do. For the past two years, we’ve been consulting for PE funds and their portfolio companies. We have strong opinions on where AI works well today, and where it doesn’t. (Case in point for the latter: Providing individual ChatGPT subscriptions makes high performers even more exceptional – but if they leave, so does the knowledge).

In working with lower and middle-market funds, we’ve identified three strong use cases for AI in PE today, and built products to solve them.

1. Sourcing: Uncover hidden deal signals at massive scale

Private equity is fiercely competitive, with far more capital chasing deals than there are truly outstanding investment opportunities. High quality proprietary deal flow is the holy grail: finding companies directly, negotiating with management teams before anyone else knows they’re in play, and closing at fair prices without competitive pressure of banker run processes to drive up valuations. Twing.AI is here to help you do just this.

Nowadays, your associates are likely using ChatGPT or a similar consumer LLM to go deep into company research – but that means you’re still limited to the subset of companies you’ve proactively flagged. Imagine running similar research across thousands of companies, while also integrating with existing data providers, to provide accurate signal on compelling opportunities. Twing.AI does just that.

We pull from company websites, reviews, local market data, news, and traditional providers, then put it together into a complete picture. Looking at an HVAC roll-up in Texas? We’ll show you which metros have the fastest-growing housing starts, relevant customer sentiment, and which owners are likely approaching retirement. Looking at specialty distribution platforms in the Midwest? You can benchmark any prospect against similar companies instantly, instead of each associate doing it their own way in their own spreadsheet.

You’ll immediately surface the information that actually matters for underwriting, not just what’s in the filed documents.

2. CIM Review: Validate deal metrics beyond the sales pitch

A hundred page CIM just came across your desk, and you already know that it’s been compiled by a bank with the goal of putting the business in a favorable light. It’s now your job to put together the missing pieces: What are the important details about the management team that go beyond what’s in the CIM? What’s the competitive landscape look like? What KPIs matter? How is growth and margins trending?Which EBITDA adjustments do we believe?

With Twing.AI, you can instantly answer those questions and more — ensuring your team spends their time on judgment calls instead of gruntwork. The platform enables volume by continually running these sourcing analyses, and ensures quality of the resulting analysis by extracting the relevant structured data. This means a normalized, reliable way of comparing deals against each other. We’ve helped deal teams cut review times by 90%+ from hours to minutes. You save hours of work, and your team focuses on deals worth their time.

3. Data Cube: Transform chaotic deal data into insights fast

You’re in the VDR and need to understand how the target is performing under the hood. You download a monthly P&L for the past five years. That’s 60 months of data, mixed between poorly formatted Excel and QuickBooks outputs across months. Sound familiar?

The manual process of stitching everything together takes hours — but you need this done ASAP to surface insights on customer concentration, retention rates, and revenue variability.

With Twing.AI, you can customize and run this analysis in minutes. You define your parameters on revenue categories and time periods you want to analyze. Select the files that contain that data (Twing.AI will recommend the relevant ones), and let Twing.AI consolidate all that data into a consistent dataset to flow into your model. Furthermore, the platform also runs preliminary analyses to surface insights you need. We meet you where you are with your preferred tools.

Helping you compete on a bigger stage

Right now, plenty of firms think they’re implementing AI effectively. What they’re actually doing is putting a language model on top of fragmented, unstructured inputs and hoping for insight. The result? Confident-sounding output that nobody trusts.

AI in and of itself isn’t the unlock here. Data and workflows are. With the proper data engineering expertise, we’ve implemented models into live workflows that generate tangible ROI for our PE clients, allowing them to compete with larger firms boasting in-house AI capabilities.

With Twing.AI, your expertise turns into real leverage: pass faster on the deals that won’t work, and lean in on the more attractive investments. When you close, Twing.AI helps you build conviction, maximize value, and help you spend more hours on deals that close.

We’ll be writing about what actually works, what doesn’t, and what we’re learning building AI inside real private-equity workflows. If that piques your interest, stick around – and be sure to reach out with any questions