useElevateAI

Company data is a mess. Different systems, different formats, constant M&A activity—keeping data clean feels impossible. Fixing it today is: * Slow (6+ months → years) * Expensive ($100Ks → $1M+) * A major distraction from running the business That’s why we built Elevate.

.@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.

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 .

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?

🚀 Infrastructure that scales with your M&A pace. Our platform is purpose-built for acquisitive companies. Each new integration follows a repeatable process, so you’re not reinventing the wheel every time you close a deal.

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

📊 Cross-system analytics that actually work. We free you from rigid legacy systems, enabling you to track the KPIs you care about—across financials, ops, CRM, and more. One centralized view, tailored to your workflow.

🔍 Full portfolio visibility, fast. With Elevate, new acquisition data—no matter the format—is standardized and available for analysis within days. No waiting on IT. No spreadsheets lost in email threads.

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.

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 🧵

Why have people been missing the most pressing problem in data? ETL—sorry, ELT—tools are everywhere. Plenty of solutions help companies move data from point A to point B. But moving data isn’t the bottleneck anymore. Transforming it is. Feeding everything into a data lake (aka a glorified cloud storage bin) is like dumping a bunch of Legos into a box. Great, it’s all in one place—but now what? You still have to put the pieces together! And analytics are great – trust me, as a former PE investor, I ❤️ a good chart! But analyzing your P&L only gets you so far. You need much deeper cross-portfolio data to generate the insights that lead to game-changing strategic actions. But making sense of the data mess is tedious, expensive, and distracting. That’s why at @useElevateAI we handle it for you. No new tools to learn, no complex configurations—just clean, enriched, usable data. Jump in! Where do you see companies getting stuck with data?

If you don’t know what’s happening, you can’t fix it. Too many businesses operate in the dark, making decisions based on incomplete or bad data With M&A, this is even riskier—misaligned customer records, inconsistent financials, and fragmented systems lead to costly errors

Go to any major business school, and you’ll hear a lot about roll-ups—i.e., acquiring and consolidating businesses. When I left my job in PE, the most common question I got was, “What are you going to roll up?” So why are roll-ups so popular? It’s basic supply and demand. Supply-side (of companies): Baby Boomers—ever heard of them? There’s a massive wave of founder-led companies whose owners are looking to retire or take the next step. Many lack clear succession plans. These founders have spent decades building businesses and are now looking for liquidity to capitalize on their hard work. Demand-side (of capital): There has never been more dry powder in PE and alternative assets. The money has to go somewhere, and one of the most obvious ways to grow equity value is through M&A—especially when multiple arbitrage is at play. Some sectors see acquisitions at 4–6x EBITDA while the platform trades at 10–15x. That's the closest you'll get to a free lunch. But here’s the catch—rolling up businesses creates a massive data problem. Every acquisition brings in new systems and processes, making it a nightmare to get clean, consolidated data. That’s exactly why we built @useElevateAI — to help PE-backed roll-ups integrate, clean, and enrich their data so they can actually realize the value they’re acquiring. What did I miss?

We work with innovative, data-driven companies (often PE-backed) that need fast, accurate, and scalable data management solutions. We’re expanding. If unintegrated or messy data is slowing you down, let’s talk.

Company data is a mess. Different systems, different formats, constant M&A activity—keeping data clean feels impossible. Fixing it today is: * Slow (6+ months → years) * Expensive ($100Ks → $1M+) * A major distraction from running the business That’s why we built Elevate.

We automate the entire data consolidation process—turning scattered, messy data into clean, actionable insights. Our AI-powered pipelines: ✅ Integrate data across systems & acquisitions ✅ Match & clean key data points ✅ Enrich datasets with valuable context