The single most important thing to know is that the companies building the underlying AI are moving up the stack and building the vertical tools themselves. Anthropic rolled out plugins for Excel and Powerpoint. ChatGPT 5.4 is highlighting their multi-format file generation and focus on professional services. Just in the past week there’s news that Anthropic is partnering with Blackstone and other PE firms to form an AI consulting venture. And two days ago you had news leaking that OpenAI is looking to do the same with TPG, Advent, Bain, and Brookfield Asset Management.

This means the application you’re paying for today is likely just a feature in the next Claude release. And it means the firms that come out ahead kept their data portable, stayed flexible on tooling, and built workflows around how they actually work.

From that starting point, three principles should guide how you approach AI for your fund — on software contracts, on data, and on implementation.

Avoid long-term contracts

Whatever tool you buy today will look fundamentally different in six months. A few months ago you paid for a separate tool to generate PowerPoint slides “using AI.” That’s now handled natively inside Claude, which carries context not just from every conversation you’ve had but also integrates with the tools you already use. The experience of doing the work inside a rapidly improving foundation model is simply better than stitching together a set of disparate tools that may or may not keep up.

Even if you’re skeptical of that trajectory, the pace of change alone should give you pause. Software engineering is the canary in the coal mine. Ask any software engineer which AI tools they use for coding and you’ll get a different answer from everyone — and a different answer from the same person six months later. Our own team has cycled through Codex, Claude, and Cursor over the past year. Preferences shift, models leapfrog each other, and the landscape reshuffles faster than any contract should assume.

Signing a long-term deal with any AI vendor right now means betting on their roadmap, which is a dangerous game to play.

Own your data

Most of these tools are a polished interface on top of the same AI models you already have access to. The cost of building custom software has collapsed — we worked with one fund that went through a full demo process with every major AI-for-PE platform and then decided to build in-house. The number of platforms competing for your attention is itself a signal of how commoditized the interface layer has become.

What isn’t commoditized is your data and your process. Your data should stay yours — made actionable by layering the right tools on top of it, not deposited into a vendor’s database where it gets structured around their choices and locked into their interface. How often have you wished for a product to act a certain way — then got frustrated when you just couldn’t get it to do a seemingly simple thing? A single vendor owning your data means you inherit their constraints every time you want to do something new with it.

The most sophisticated customers we work with have internalized this. They’re moving away from vendor UIs and toward direct API and MCP access — building lightweight interfaces around their own data rather than the other way around. The work happens inside foundation model chat apps, integrated directly with their data. The UI is almost an afterthought.

Build for your process, not theirs

Generic platforms give every fund the same experience. Your firm’s edge is in the specifics: your thesis criteria, workflows, and decision process — in other words, your institutional memory that currently lives in historical files and team member’s heads. Off-the-shelf tools don’t capture that, and without it they don’t get adopted.

The firms doing this well are rethinking how they operate, identifying where AI actually accelerates their process, and deliberately building those workflows. Concretely, that looks like:

• Automatically drafting first pass versions of memos and one pagers: As new CIMs arrive, their system extracts key data and generates one pagers designed around the way they think and presented in the layout they’ve spent decades perfecting. This data is also stored in a structured way, giving them the ability to compare against similar deals and call out any anomalies.

• Ingesting data room documents and automatically categorizing and summarizing each one: The results of this analysis reduce the friction for everyone on the team, since everyone can see what’s available and speed up the diligence process.

• Kicking off deep research tasks based on specific triggers: For example, pulling in the background and experience and doing a litigation scan for the executive team.

The ideal solution is a best of breed integration. The underlying capabilities move so quickly that you need to design your system to be flexible and focus on the parts that will not change - the data and the workflows - and then layer on evolving improvements

How to evaluate what’s in front of you

Given all of this, comparing features across platforms is the wrong approach. Any third party product will end up being too generic for your firm’s specific MO. Given that, the right questions to ask are:

1. Can you take your data with you? More specifically, can you export everything the tool has analyzed? Can you access the structured data through an API or MCP? If the vendor disappeared tomorrow, would you still have your intelligence?

2. Does it bend to you, or do you bend to it? Can the tool be configured to match how your firm actually works — your IC format, your processes, your thesis criteria — or are you reshaping your process to fit their product?

3. Can you swap out the AI model when something better ships? Are you locked into one model, or can the system be pointed at whatever performs best for a given task?

The firms that answer those questions well will have built something that keeps working regardless of which tool wins.

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

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