Joshua Delekta, MBA

Using Snowflake Intelligence for portfolio management. Everyone in asset management industry and wealth management industry need to see this. Particularly if you work in data. Don't worry you will still need a portfolio manager. But having a tool like this was unimaginable last year. And yet it is a reality this year. Amazing demo! Thanks Prasanna for recording this. Very, very useful. #AssetManagement #WealthManagement #InvestmentManagement #PortfolioManagement #Snowflake #AI #BI #BusinessIntelligence #Analytics #DataWarehouse

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We've spent the last 6 months talking to PE firms across Connecticut. One thing keeps coming up: LP reporting is taking way too long — and it's not because the data doesn't exist. It's not a people problem. It's a data infrastructure problem. Most firms are still pulling numbers from 8–12 different sources — portfolio company spreadsheets, accounting systems, deal trackers — and stitching them together manually every quarter. By the time the LP package is out the door, the team has spent 3–4 weeks on it. And if an LP calls with a question two weeks later? That process starts all over again. Here's what the firms addressing it have in common: They've stopped treating LP reporting as a one-time quarterly event. Instead, they've built a single source of truth across their portfolio — one that's always current. Their IR teams can answer LP questions in minutes, not days. Their annual meeting prep takes days, not weeks. And when LPs see that kind of responsiveness? It changes the relationship. It's not a technology overhaul. It's a structural shift in how portfolio data is organized and maintained. If you're heading into annual meeting season and still living in spreadsheets — happy to trade notes on what we're seeing across the region here at cofi.ai. #privateequity #portfoliomanagement #LPreporting #connecticut

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Not all guidance is created equal. Some is explicit. Some is buried in management commentary. Companies quietly adjust revenue ranges, margin expectations, demand commentary, or segment outlooks - sometimes just a sentence or two that changes the entire forward story. Hudson Labs has a proprietary model designed to detect ALL forward looking statements made in an call. - Guidance revisions (raised, lowered, or reframed) - Subtle outlook changes in management commentary - The catalysts likely driving those revisions - Similar shifts happening across peers and industries Instead of manually digging through transcripts and filings, you can immediately see what changed, why it changed, and where it might show up next. Because the market doesn’t move on the past quarter. Below is an example on ORCL (Oracle), who reported last night. https://lnkd.in/eD_4EdFh

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What happens when you let Bigdata.com loose on Palantir’s earnings call? Investors love numbers. But tone tells the real story. I used Bigdata.com to analyze Palantir Technologies’s Q3 vs. Q2 2025 earnings calls, not just what the execs said, but how they said it. Through Bigdata’s trusted access to Quartr’s verified financial content, I could pull audio transcripts, management decks, charts, and filings (10-Q, 8-K, etc.) and analyze them all in one place, with confidence that the data was accurate and complete. Bigdata even interpreted the visuals in the Mgmt powerpoint, turning messy data into context it's LLM could actually read and interpret. Here’s where it gets spicy: 1. Q3 wasn’t just good, it was victory lap energy. CEO Alex Karp didn’t just report record results. He declared them “arguably the best any software company has ever delivered,” pointing to a Rule of 40 score of 114 and 100%+ U.S. commercial growth. In Q2, he was confident. In Q3, he was triumphant. Think “from proving ourselves” to “we’ve arrived.” 2. AI adoption is no longer hype, it’s revenue. Both Karp and CFO David Glazer doubled down on Palantir’s AI-driven growth story. No contradictions, no backpedaling, just a louder drumbeat of conviction. 3. U.S. commercial is the new growth engine. Government still matters, but the narrative has shifted. The contrast between “stagnant Europe” and an “American AI revolution” says a lot about where the next leg of growth is headed. Why this matters: Anyone can read a 10-Q. Very few can hear when a CEO’s tone shifts from cautious optimism to absolute conviction. That’s often the inflection point, when confidence turns into momentum, and momentum turns into returns.

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Snowflake ’s deal with OpenAI is being talked about like another AI partnership. It’s not. This is really about where enterprise AI will live. Enterprises don’t want their data bouncing between tools, models, and clouds. They want AI inside the platforms that already hold their most sensitive data — with security, governance, and compliance baked in. By bringing OpenAI models directly into Snowflake, the message is clear: • The model matters less than the data • The winner isn’t the smartest AI, but the most trusted platform • Enterprise AI is shifting from experimentation to infrastructure This isn’t a chatbot story. It’s an ownership and control story. And it’s a sign that the enterprise AI race just entered a very different phase.

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Looking forward to speaking at an upcoming Snowflake conference on how AI is changing investment research workflows. The buy-side landscape is evolving as firms are increasingly working to unify structured and unstructured data sources to generate faster insights. There's significant value in unstructured data: earnings transcripts, analyst calls, and company filings contain market signals that have traditionally been difficult to analyse at scale. I'll be joining speakers from Snowflake, Alpha FMC, and Third Bridge to discuss how investment teams are using AI and LLMs to: -Extract insights from unstructured text -Identify sentiment shifts and competitive dynamics -Build more efficient research workflows My session focuses on "Questioning the Answers: LLMs Enter the Boardroom" research that uses AI to identify executive behaviour on earnings calls that correlate with company performance. Register here: https://lnkd.in/e2sZ4MQX #InvestmentResearch #DataAnalytics #AI #Snowflake

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❄️ Snowflake Moving into BXP, Inc.'s 7 Times Square While the Manhattan office market remains "delicate," cloud computing giant Snowflake is heating things up with a major new lease. The data platform has officially signed for 83,000 square feet at BXP’s iconic 7 Times Square. The Move: Snowflake is relocating from its nearby Bryant Park offices at 114 West 41st Street to level up its NYC footprint. The Building: They join a heavy-hitting roster at the 1.3M SF tower, including KnitWell Group and Friedman Kaplan. The Perks: BXP recently upgraded the property with a new client lounge, cafe services, and premium conference spaces. The Context: This follows Snowflake's massive 773k SF sublease in Menlo Park last year—proving that while they are "cloud-based," their physical office strategy is still scaling fast. Average asking rents in the Times Square submarket have been hovering around $76.42/SF, signaling continued demand for well-located, amenity-rich Class A space. https://lnkd.in/gyY2NWjd

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Great post. Increasing revenue, reducing costs, and saving time should be the core objectives behind every AI initiative. Since time is money, prioritizing time efficiency is never the wrong target. Time saved allows people to operate at a higher level and frees them to build more meaningful interactions and connections.

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