Deck AI

ChatGPT writes a better first draft analysis than most analysts. It also can't reliably extract numbers from a scanned PDF. When deal teams see AI-generated analysis with incorrect figures, they blame the model. But trace the error back, and you'll find it's likely that it originated during document processing, before the model ever started analyzing. We've been testing frontier models on real financial documents and found a consistent pattern: the analysis reads well, the confidence scores look fine, but the underlying numbers can ultimately be wrong. Models drop minus signs from income statements, misalign table columns, and introduce digit-level errors in extraction. This happens because general-purpose vision processing optimizes for understanding concepts, not preserving pixel-level precision. Features that matter in finance like decimal points, negative signs, subscripts exist at resolutions these systems sacrifice for efficiency. It's like reading through frosted glass: you can see enough to reconstruct something plausible, but not enough to guarantee accuracy. Standard AI benchmarks don't catch this because they test comprehension using clean documents. Real workflows involve scanned PDFs, compressed filings, and hybrid image-text formats where these failures show up reliably. We've developed approaches that significantly reduce these errors and are continually optimizing. The takeaway here is AI's reliability problem in finance isn't mainly about reasoning. It's about whether models can accurately pull numbers from real documents in the first place. Bad extraction creates bad outputs, which lead to bad decisions. By the time you're questioning the AI's conclusions, the damage already happened upstream.

At Deck AI, we are treating AI for knowledge work with the same rigor others reserve for scientific inquiry. That distinction matters. Knowledge work is not a formatting problem. It is a reasoning problem. How to structure understanding itself in a way that is transparent, verifiable, and cumulative. This is the foundation of our newly formed research division: Deck Labs. Our research is focused on reasoning systems that preserve the structure of human thought, not flatten it. The goal is not to replace judgment, it is to strengthen it, to give professionals a system that reads across messy source material, preserves the details, and returns structured conclusions that can be checked. Deck Labs exists to bring a deeper intellectual standard to how machines assist human reasoning. We are not chasing fluency or speed. We are building systems that earn trust through rigor, clarity, and discipline. You can find our first brief here: https://lnkd.in/eygpSJc6

Today we’re introducing Deck Labs, our internal AI research division devoted to advancing AI-driven structured insight for knowledge work. Deck Labs exists to pursue a singular mission: to build the operating system for knowledge work. Not a metaphorical one. A real, composable system that can read, reason, and synthesize across the unstructured sprawl of modern organizations. From diligence to deal review, compliance to capital deployment, our research centers on one question: how can machines generate structured insight that is trustworthy at scale? We take a different stance from the prevailing “replace the workforce” ethos. Where others aim to automate judgment, we aim to amplify it. Deck’s research is built on the principle that the highest value of AI lies not in substitution, but in structured augmentation. The systems that make human reasoning faster, more complete, and verifiable. In regulated, high stakes, and fiduciary contexts, blind automation isn’t progress. It’s risk. Precision, transparency, and alignment with human intent are the real frontiers. Our team brings together researchers and engineers from applied AI, enterprise systems, and quantitative finance. We have worked at the intersection of reasoning models, retrieval architectures, and secure inference, developing methods that make large-scale reasoning auditable, composable, and verifiable. We believe that reliability, not raw capability, defines the future of intelligent systems. As our first public contribution, we are releasing The AI Danger Zone: Why General Models Fail on Financial Documents, a research brief uncovering why today’s general-purpose models stumble on the details that matter most in finance. Through controlled testing across frontier models, we identify consistent failure patterns in numerical extraction and present early architectures that begin to close those gaps. Deck Labs will publish ongoing research and prototypes demonstrating how structured AI reasoning can augment professionals across finance, law, real estate, and strategy. We will share our work in open technical notes and experimental interfaces that reimagine what it means to think with machines. Where others see AI as a substitute for human reasoning, we see it as a structured extension of it. Read more here: https://lnkd.in/eSxkJgi5

Data accurate insights to do better work.

Most VPs & associates I talk to are saving 3–4 weeks of work using Deck AI. Not minutes. Not days. Weeks. — Here’s the exact deal stack making it possible: - Affinity.co → CRM that keeps sourcing and relationships organized - ChatGPT (or Hebbia) → external market research and data gathering at scale - Deck AI → end-to-end deal execution, from messy data rooms to IC-ready artifacts —  How the process works: First - Upload the data room and external research from ChatGPT and prospect notes from Affinity.co to Deck Deck parses PDFs, PPTs, Excels, virtually any unstructured data into structured facts. No manual scrubbing. Second - Within minutes analysts review a full IC draft, legal analysis, first pass memo, etc Core logic, comps, risks, upside are all organized in a word doc or ppt that’s 70-80% ready and data accurate (thanks to our amazing data scientists) Day 3 - Iterate with colleagues and stakeholders Focus on judgment and insight, not spreadsheets. The grunt work is already done. Use Decks' Deep Dive chat feature to button up any other outstanding diligence or questions from the CIO. These are the types of knowledge based tasks where AI thrives. It’s collapsing weeks of diligence into minutes , freeing deal teams to spend time where it actually compounds. Because time is money, of course. The best part is Deck is easily mapped to virtually any workflow that needs automation. We are reimaging an operating system for professional judgment by automating the messy middle of professional work.

Work smarter.

Buy side funds, this is for you. Yes, you already have ChatGPT. But we built Deck anyway, because: - We were tired of wrestling CIMs, LP letters, and management decks into one coherent view - We wanted your deal team to always walk into IC with context in hand, not scattered notes - We hated seeing patchwork, vibe-coded solutions (no offense) collapse at the worst moment - We found internally built AI projects quickly turned into a McKinsey retainer - We wished automating core workflows was as simple as Stripe, not stitching together macros - And every “AI for finance” pitch seemed to just repackage your CapIQ or PitchBook license So we built something better. Let us show you. More in the comments.

Want to 10x your output? We can help. Much of the AI conversation is about cost reduction. It's understandable. But at Deck we think about 10x differently: - 10x your deal flow. When preliminary research and memo drafting take hours instead of days, you can evaluate more opportunities without expanding headcount. - 10x your responsiveness. Senior professionals expect timely analysis. When an IC meeting moves up or a new deal lands Friday afternoon, the ability to quickly synthesize research and produce a coherent investment memo becomes a competitive advantage. - 10x your strategic thinking time. The real value in your role isn't formatting slides or consolidating notes from data room reviews. It's the analysis, the pattern recognition, the judgment calls that come from experience. Automation handles synthesis. You spend more hours on work that actually moves the needle. Private equity and private credit diligence requires judgment, context, and nuanced understanding. The question is simpler: what tasks genuinely require your expertise, and what tasks are necessary but repeatable? When teams spend 60-70% of their time on document preparation and research consolidation, there's significant capacity locked in process work. Tools that help automate investment memo creation free up that capacity. The firms seeing the most impact aren't using AI to do less with less. They're evaluating additional opportunities, digging deeper on promising deals, and maintaining quality standards even as deal flow increases. That's the real 10x.

Meet Deck, the AI agent made for deal teams, engineered for everything. Join the waitlist. https://lnkd.in/e9iEWTxS

Deep Dive with Deck handles routine tasks like drafting emails to tackling all the questions from IC. https://lnkd.in/dkhg9XsS