Scott Cara

@JesseTinsley this is what my YC-backed start-up does. I'm ex-PE and lived this problem for years. Happy to chat useelevate.dev

idea i was riffing on today… AI for private equity and rollup integration. Thoma Bravo for AI era carve outs and roll up integrations. Could run a berkshire, constellation software or thoma bravo with a few dozen employees and automate everything else internally and at portfolio companies. Going to tinker with this idea 🤔

$3 Trillion is currently stuck in unpaid invoices. An average invoice takes 59 days to clear. If you're doing >$10M in revenue, getting paid in 30 days instead of 59 days will literally make you millions over 2 years. Introducing Monk. It collects your money faster and pays for itself in 30 days, guaranteed. Fast-growing companies like ElevenLabs and Profound rely on Monk. The Problem: 39% of the $3T stuck is due to two stupid reasons: 1. "Please fix this comma in the invoice and then we'll pay you'" (this happens 2-3 times per transaction) 2. "Sorry the payment reminder got buried in my inbox" (automated emails get ignored) A payment that should take 2 days, takes 10 days. Your cash on hand is embarrassingly behind "recorded revenue". To fix this, we raised $4M led by @btv_vc with participation from @gtmfund and @danonanthony. For our first product, we had to innovate on 3 dimensions: 1. Monk turns signed contracts into invoices with near-perfect precision - We leverage frontier models to extract key terms from your deals and turn them into invoices. 2. ⁠Monk collects payments agentically with a 24% better reply rate than automated emails: - Tuned to write emails that feel like a human request, not spam. Your invoice is competing with everything in their inbox. - We know how to find an alternate point-of-contact when someone is OOO, when to reach out, and what tonality yields a higher response rate. If you had someone on your team whose invoice emails got answered 24% more than others - that would be a big deal. You’d make them the head of revenue collection. 3. Granular visibility into your cashflow: - Understand your cash position, aging, or expansion/contraction at customer level. Book a demo at monk.com/book-a-demo-fo… and we guarantee that Monk will pay for itself within 30 days. If you've read this far, we are giving away 40 water-tight contract templates. This would cost >$10K to any agency, startups, or freelancer, to create from scratch. Retweet this and comment 'Monk' and we'll send them to you.

@theBhulawat @southpkcommons very cool! this feels like the future we want. excited to try it out

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.@useElevateAI is an AI-powered data management platform built for roll-ups—helping PE-backed companies integrate faster, unify messy data, and unlock AI. Elevate is already powering PE-backed consolidators in HVAC, pest, and restoration.

Everyone is rushing to figure out how to use AI to improve their businesses. At @useElevateAI, we call ourselves AI-powered—and that’s not just a tagline. AI is foundational to our company: it’s how we built the product, and it’s how we deliver for our roll-up customers. We use AI across three core areas of our product: 1. API calls – We use LLMs and agentic workflows to extract and process data from any format, clean messy fields like customer or vendor names, and enrich datasets with new information (e.g., HHI, industry). Doing this at scale wasn’t possible before AI. 2. Coding – AI helps us write and maintain our data pipelines by stitching together reusable code blocks we’ve developed—some human-written, some AI-generated. While we still manage the complexity closely, AI is increasingly contributing to our codebase. 3. Analytics + QA – We use AI to detect anomalies, flag inconsistencies, and identify gaps in customer data—ultimately helping us deliver cleaner, more reliable outputs. It writes SQL well, but requires guidance on the analytics and sense checks that matter most. MCP has been a big step forward here. AI also powers how we run Elevate’s day-to-day: GTM – We use it to streamline outbound, identify prospective customers, and accelerate content creation for messaging, emails, and collateral. Admin – AI acts as a co-pilot for operations—helping us organize and work through the chaos that comes with building a company. We’re still learning, too—please comment or reach out with other ways we should be using LLMs

More data ≠ better decisions. Data is a tool—when wielded by the right people, it creates competitive advantage. At Elevate, we focus exclusively on making your data useful—identifying what matters, then wrangling information across systems and entities into a clean, enriched, single source of truth. Some of the most impactful metrics we help our customers track: * Post-acquisition churn * Cross-sell opportunity * Share of wallet expansion * Net revenue retention * Customer cohort performance * True direct margin * Performance by technician or sales rep * Vendor overlap + savings potential None of this is simple—especially for acquisitive roll-ups—but our AI-powered pipelines and overqualified engineers are built for this.

Oh, Frankenstein companies 🧌... You might know the type: stitched together by M&A, no central source of truth or decision-making—just chaos hidden behind a pretty CIM, expecting significant multiple arbitrage. The process feels great until the data room opens. Then, the wheels start falling off: "Wait, what is EBITDA -- and why do these two files say something different?" "We really can't compare performance across divisions?" "Do we not know our sales for this customer across the whole company?" In private equity, valuation isn’t just about EBITDA—it’s about how defensible that EBITDA is (see: multiple). Nothing spooks PE buyers faster than inconsistent, disparate data. On the flip side, if you do have clean, consolidated data in a PE process, it can make a real impact on your exit value because it: 1. Proves operational credibility. Buyers see disjointed systems and manual workarounds as risk. Clean data signals a well-run, integrated operation—and builds confidence in the numbers. 2. Sharpens the narrative. The best exit stories are data-backed. Revenue by industry, churn by cohort, direct margin by customer—if the data’s not clean, the story falls apart in diligence. 3. Protects your management team. Exit processes are sprints. Execs are already sitting through 4+ hour management meetings, diligence deep dives, and on-site visits. If you’re still building dashboards or chasing reconciliations mid-process, you’re burning time—and value. Don't be a Frankenstein company -- check us out at @useElevateAI .

I get asked a lot how much AI I use to write these posts—so I thought I’d share my process. If you’re experimenting with AI in your writing, here are my three biggest tips: #1 – Start with a spark that comes from you. The idea or insight has to be yours. It doesn’t need to be more than two sentences—just something thoughtful and original. Asking an LLM to write a post from scratch is a recipe for something bland and forgettable. I always begin with a quick insight—often pulled from the hundreds of conversations we’re having with customers and pilots at @useElevateAI. That’s the fuel AI needs to be useful. #2 – Use AI to expand and structure your thinking. Once I’ve got the idea, I feed it into a custom GPT we built called “Chad, Elevate’s CMO”—named after the toga-clad deer below. Chad has access to our overview materials and internal strategy docs, so it understands what Elevate does and how we add value, and specific instructions on our goals. I give it the high-level idea, some context on the audience or goal, and Chad comes back with a longer post draft. #3 – Make it yours. Then loop back for polishing. I never post Chad’s draft as-is. It’s usually a good starting point, but much too generic. I rewrite it in my own words, sharpen the insight, and then send it back to Chad for polishing. AI helps tighten the structure, improve clarity, and make the copy more concise. Then I read it through once, make some final edits, and click post. Curious how others are using AI in their writing. Drop a comment or ping me if you’ve found tactics that work.

As a tech founder with a PE background, I often describe my role as part translator — helping private equity firms and PE-backed companies identify where AI can actually drive value. Data is a big one — and it’s why we’re building @useElevateAI : an AI platform that integrates, cleans, and enriches messy data across disparate systems of record. But in talking to thousands of folks and working closely with a range of businesses, I’ve also seen other areas where AI is creating value. Here are a few: 📊 Finance automation Invoice processing, collections, GL cleanup — AI agents are starting to chip away at back-office automation. Especially helpful in high-volume orgs. 🎧 Customer service automation A crowded space, but still useful. AI-driven chat and ticketing tools are improving resolution times — though as Klarna’s walk-back shows, full automation has limits. 🔍 Research & market intelligence AI tools are speeding up commercial diligence by summarizing 10-Ks, drafting memos, and benchmarking peers — especially useful for leaner investment teams or early market research, less effective (so far) in deep diligence. 🤖 Investing copilots I’ve seen early versions of AI copilots that sit in on investor calls and suggest questions or red flags in real-time, based on prior notes and transcripts. Still emerging, but promising. 💼 Sales enablement AI is helping sales reps draft quotes, personalize follow-ups, and prioritize leads — startups here are moving fast and there are a lot of them. I seem to get pinged by a new one every day. 📦 Inventory management More machine learning than generative AI — but tools that forecast demand and reduce overstock are creating big margin gains for inventory-heavy sectors. There’s plenty of hype in AI, but these are real, operational levers driving enterprise value. If you’re exploring any of these spaces, I’m happy to connect you with startups doing interesting work. Comment below or DM me. What areas did I miss?

If you’re gearing up—or already in-flight—on a PE-backed sale process, you need to be ready for the level of scrutiny that buyers (and sellers) demand. Financials are just the starting point. You’ll be fielding questions on comparable sales, revenue retention, and CAC—not just in aggregate, but broken down by region, industry, and product line. And buyers expect answers fast—backed by clean, credible data. If your data lives in multiple systems, or your customer list includes four versions of the same name (“Marriott,” “Marriott Hotels,” “Sheraton”)… you’re not ready. If your team is stitching together reports manually, you’re burning time in a process where speed and clarity directly influence valuation. I’ve been on both sides of the table—buying and selling PE-backed assets. The best outcomes don’t go to the companies with the best story. They go to the companies with the best data to prove the story. That’s where @useElevateAI comes in. We help PE-backed companies consolidate, clean, and enrich their data—so when it’s time to engage with buyers, your systems are buttoned-up and your metrics are airtight. We’re working with teams thinking ahead to exit—because clean data isn’t just a back-office task. It’s strategic. It’s the difference between a good exit and a great one.

The #1 takeaway I’ve heard after talking with 100's of roll-ups and consolidators: “This is harder than we thought.” That doesn’t mean it’s not worth doing. But if you’re stepping into a roll-up strategy—whether in HVAC, pest control, accounting, or healthcare—go in eyes wide open. Here’s what makes roll-ups hard: 1) Deal-making is a grind Getting to a letter of intent (LOI) takes relentless sourcing and relationship-building. Closing requires patience, analytical rigor…and plenty of legal and accounting fees. 2) Post-close performance is fragile You just spent millions on a business that’s now underperforming? You need to keep key people engaged and track performance across systems that don’t talk to each other from Day 1. 3) Exits demand precision story-telling You’ll never be scrutinized harder than during a financing or sale. A messy data story will cost you. A clean, credible one builds buyer confidence. We built @useElevateAI because too many great operators were flying blind. Our AI-powered platform helps you integrate, clean, and enrich data—so you have the visibility and story you need at every stage. What else have you experienced or heard that makes consolidating so challenging?

Leading PE-backed roll-ups are investing early in scalable data solutions. Here’s what the best-in-class approach looks like:

To integrate or not to integrate—that is the (multi)-billion-dollar question for roll-ups. After the rush of a new acquisition, it’s tempting to take a breather. But what happens next determines whether the value you just paid for gets amplified—or lost. At one end of the spectrum: “do no harm.” At the other: full consolidation, fast. Most operators land somewhere in between and there’s no one-size-fits-all, but some guiding principles help: 1) Distinguish platforms from tuck-ins: Platforms (e.g., regional hubs) may retain internal systems and processes longer; tuck-ins typically benefit from faster alignment. 2) Empower an internal lead: Give someone at the acquired company ownership of integration. It builds trust and drives results. 3) Be clear on what matters most: The top concern for employees? Pay and benefits. Don’t change them—unless it’s an upgrade. 4) Move fast, but not recklessly: Full historical data migrations slow you down and introduce risk. Focus on the data and workflows that matter most. That’s where @useElevateAI helps. We use AI to unify, clean, and enrich data across systems—so you get portfolio-wide visibility without waiting on full migrations or manual consolidation. Integration doesn’t have to be all-or-nothing. And it doesn’t have to wait.

A friend and I joke there are two PE outcomes: - Revenue grows, margins expand, multiple rises → good deal. - Revenue stalls, margins shrink, multiple drops → bad deal. This B-school humor has a simple truth: PE portfolio performance hinges on three levers: 1. Revenue – How fast are sales growing, organically and via acquisitions? 2. Margin – Are gross and EBITDA margins improving? (Revenue + Margin = EBITDA growth) 3. Multiple – What’s our EBITDA multiple? The first two are measurable, but most portfolio companies struggle to explain why they’re changing—especially under PE board scrutiny. The third is trickier. Market dynamics are beyond control, but one thing isn’t: Data quality. Clean data → stronger buyer confidence → higher exit multiple. Reach out to us at @useElevateAI to see how we’re helping PE-backed firms improve their data.

Ex-private equity founders are a relatively rare breed for a simple reason: The Excel math doesn’t look good. You’re leaving a relatively safe, high-paying job where there’s a proven path to a comfortable lifestyle* to enter the jungle of uncertainty. Or put in finance jargon, the risk-adjusted return doesn’t pencil. From a financial outcome perspective, that’s all true…but like most spreadsheet models, they miss a key input: utility. The simple model lacks the intangible assets you gain from entering the start-up void: ✔️ The mindset shift in how you approach problems from task doer to task maker ✔️ Learning what it takes to build something from scratch that people will pay for ✔️ The many joys (and terrors) of being your own boss So does it make sense? Probably not. But it's the path we chose and it's one hell of a journey. Follow along at @useElevateAI and comment with your journey.

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That said, I’m bullish on a few angles in finance: ✅ Selling into PE-backed companies (our focus at @useElevateAI ): PE firms put significant pressure (and incentives) on their PortCos to move quickly and be analytical. That creates a great environment for innovators. ✅ Fund back-office tools: Compliance, LP management, valuations—these are real pain points with limited industry standardization. ✅ Higher-volume finance: Traders and hedge funds lack some of the problems I highlighted above. But be ready for the retort: “If your tool is that good, why aren’t YOU trading with it?”

Why its hard: 1. Incentives: Every $ spent on fund-level tech is a $ out of the GPs’ pockets. It’s not like selling into a company where cash flow doesn’t directly accumulate to the buyer. The owner is the buyer. 2. Tools for associates: Most tools I see aim to replace or augment associates’ work. But i) associates rarely make purchasing decisions and ii) PE is an apprenticeship model. Downsizing associates means you lose your best supply of future VPs, Principals, and MDs. 3. The work itself: PE is about relationships, negotiation and analytics. Tech hasn’t cracked the first two (although I’ve heard some clever ideas - Brian Bolze 👀) and Excel spreadsheets dominate in quant analytics. There are some tech use cases for things like summarizing a dataroom, but that’s only so much value-add.

As one of a small group of founders with a private equity background, my @ycombinator and @southpkcommons peers often ask me about selling into PE firms. The first thing I say: take my advice with a grain of salt—this is one person’s perspective. But since I've spent most of my career at the intersection of finance x tech, here’s what I share. Selling into PE funds directly is hard (though not impossible) for 3 reasons 🧵