Camille Pfeiffer, Losange Finance and Steven Greenberg, Totem VC.
As AI attracts hundreds of billions in capital aimed at automating and infiltrating global industries, venture capital faces headwinds: portfolios are expanding in size and complexity and Limited Partner expectations are rising.
Meanwhile, the industry responsible for betting on the future is running on aging technology, fragmented data, and manual processes.
It’s a paradox hiding in plain sight and in need of urgent resolution.
After years of placing bets on founding teams building next gen AI, it’s VC’s turn to apply next generation tech to overhauling core functions from investing to data collection to LP relations.
Removing Pain Points: Easier Said, Must be Done
When it comes to unlocking competitive advantage and accurately mapping out sectors ripe for disruption, data is the wedge and the moat. But data in VC remains fragmented to an extreme degree across firms of all sizes, from seed funds to early stage shops to large, private equity-adjacent collectives.
We have first-hand experience with the data fragmentation struggle.
Years ago, Steven worked for an early stage VC firm based in New York with a fresh batch of promising seed investments. Soon after term sheets were signed and deals announced, internal logistical headaches skyrocketed. Steven and his VC colleagues had to keep track of the daily movements of dozens of companies. In practice, that required piecing together scattershot details from emails, spreadsheets, board decks, and ad hoc updates from investors, support staff, and founders.
After picking his head up to look for a vendor with a quick fix, Steven came to realize that CRMs weren’t flexible enough—they were built for sales pipelines, not fund administration or investment operations. Accounting software captured back office financials and details the firm’s CFO cared about, but it ignored performance metrics and developments that mattered to founders and LPs with shared interest in company success.
Camille approached this issue from the fund admin and operations side of VC. Prior to enlisting AI for VC purposes, Camille held responsibility for collecting, analyzing, and surfacing data manually—and almost always reactively. The process was painstaking, error-prone, repetitive, and exhausting. Like many investors and associates before and after her in-house stint, Camille spent more time chasing data than using it to inform investment strategy or support portfolio founders.
Firm Demand for Change
Until recently, keeping portfolio-specific data scattered was intentional. VCs earned reputations by pairing subjective intuition with hints of objective validation from closed door conversations, IC memos, and back office diligence.
But as portfolios have expanded and headcount has not, data collection in VC has become an exercise with critically low return on time investment.
There’s also rising pressure on firms to differentiate from peers while rigorously attracting founder interest and consistently meeting or exceeding LP expectations. Winning requires meticulous reporting at odds with manual processes and version control consistently plaguing firms.
Even when VC firms successfully collect portfolio data and format it in an intuitive way, there’s a good chance it will either sit idle or quickly become outdated. Issues that can only be solved with AI and efficiency.
The Case for a VC Operating System
The cruel irony here is that VC firms of all sizes sit on a treasure trove of market-moving data.
Promisingly, there’s been an explosion of tools in the VC space over the past few years. Ironically, instead of solving the problem of fragmentation, it’s made data even more scattered—in the process, magnifying the industry-wide need for a unifying layer.
Imagine unlocks when VCs are able to use AI to collect and contextualize all portfolio company and fund-level data into a single, daily-use environment. Firms that take advantage of such an AI platform won’t just reduce friction and permanently topple data siloes—they’ll gain swift and definitive competitive edges over formidable peers. Among them:
- AI delivers time savings: Instead of associates and analysts manually copying in data from income statements and balance sheets, AI can scan and extract critical metrics like revenue, EBITDA, and net income instantly. Analysts and associates, meanwhile, can drop version control duty to focus on higher-order work—evaluating trends, pressure-testing assumptions, and supporting founders.
- AI enables faster, sharper decision-making. Portfolio health data is updated and shared across the firm in real-time, allowing partners to act decisively on valuations, capital allocation, and startup support needs. With AI, company updates can flow seamlessly into an internal newsletter delivered to approved inboxes, ensuring everyone is in the loop on a portfolio company’s trajectory.
- AI upgrades the GP-LP relationship. By pulling together data from financial statements, investor updates, and other sources, AI can draft LP letters that are most of the way there. The goal isn’t to automate communication but to accelerate it without losing what makes VCs credible: a mix of humanity and vision. GPs are left to focus on tone and nuance while AI does the data and narrative heavy lifting. LPs benefit immensely from timely, consistent, and transparent VC updates backed by bulletproof data that builds trust with each dispatch.
- AI extends beyond fund operations into portfolio support. AI delivers insights faster, allowing founders more lead time to course-correct strategies, pivot business models, or double down on go-to-market motions when it makes sense. The right signal, surfaced at the right time, can mean the difference between a company missing a quarter—or capturing the opportunity that defines the trajectory of the fund.
Sounds ideal, no? Good news: this isn’t theoretical. It’s what we’re doing with Totem VC and Losange Finance. Our solutions not only incorporate advanced AI into legacy VC workflows, they layer in years of in-house VC experiences that embed nuance and context out-of-the-box.
In practice, VC firms use Totem VC to prep for board meetings, investment committee reviews, fundraising (with added value leading into follow-on rounds), and LP marketing. When it makes sense, they enlist the help of firms like Losange to round out the data services and infrastructure supporting quarterly reporting.
In addition to creating a clear advantage over firms still taking manual, highly-fragmented pathways to data collection and mobilization, optimizing AI sends an important signal to institutional LPs: Our VC firm isn’t just picking winners, we’re guiding portfolio companies to exits, IPOs, and industry success with the world’s best technology.
In the near future, data will be streamlined and VC use of advanced tech for in-house operations will become table stakes. But the real opportunity to transform the VC industry in the next few months will be solving how AI can transform the way investors put that data to work. It’s time for VC firms to walk the walk by applying advanced AI developed by startups they fund to their own operations; converting scattered data into a competitive edge.
Steven Greenberg is the Co-Founder of Totem VC, an AI solution provider built for VCs by VCs based in New York City.
Camille Pfeiffer is Chief of Staff at Losange Finance, a fund services firm focused on VC back office operations and fund administration based in Montreal.
