Pascal AI Labs

Gemini 3 is now live in Pascal AI, bringing even stronger reasoning and financial document intelligence to the platform. After delivering 2× improvements in parsing accuracy with Gemini 2.5 Pro across complex financial document workloads, we’ve continued pushing the frontier of what’s possible for investment teams. With Gemini 3, financial teams gain materially faster breakdowns of complex models and cleaner extraction of valuation and KPI signals, driven by: - High-precision parsing across charts, tables, and earnings materials - More accurate extraction of key values across statements and models - Stronger multimodal reasoning that reduces noise and improves reliability across workflows Together, these advances push Pascal AI closer to our goal of near-100% parsing accuracy for complex financial documents. We’re excited to keep building on the momentum of our collaboration with Google. Read more about our work together in Google’s showcase: https://goo.gle/4nLEjBQ #Gemini3

Excited to publish some of our work with Google building a high accuracy document intelligence system for financial analysis use cases. Gemini 2.5 Pro has enabled us to 2x our accuracy when parsing complex charts, tables, and graphs and deliver more signal to our customers across our agentic suite. Looking forward to deepening this partnership! If you're an investment fund looking to understand the latest in financial AI and accelerate your AI adoption journey, reach out to us today! #finance #ai

Proud to see Pascal AI Labs featured by Google AI for Developers. This isn't just an incremental update; it's a step-change. We're leveraging Gemini 2.5 Pro to deliver 2x more accuracy in financial document intelligence. While other models hallucinate on complex charts, our knowledge graph is built on an accurate, verifiable foundation. This is the new benchmark. #AI #Fintech #PascalLabs #GoogleAI #Gemini #Innovation

The biggest pattern we’ve seen this year working with leading investment funds: AI only succeeds when it has an internal Champion willing to push it into the workflow. Every successful pilot we’ve seen has this internal AI Champion – it could be a senior partner, a CIO, a portfolio manager, or a research analyst. The title is less important than the character: someone curious enough to experiment, patient enough to iterate, and empowered enough to push through organizational resistance. They don’t just sponsor the project; they protect it. Without that kind of personal ownership, even the best-built systems stay stuck in “interesting demo” mode. We have the data to prove it: with an active Champion, full-scale deployment is often achieved in under 8 weeks, with clear ROI payback in less than 4 weeks. Without one, deployments can drag past four months, and payback takes 2-3x+ as long. At the enterprise level, AI is still experimental – not the tech, but the thinking around its deployment. There’s no fixed playbook yet. The real challenge is integrating AI into human reasoning, not replacing it. When the Champion signals curiosity instead of skepticism, everything shifts. Teams engage differently. We've even seen funds where Champions have created internal leaderboards of AI usage, turning adoption into a competition and rapidly gathering the critical feedback the model needs to learn how to think like the fund. For startups building in this space: enterprise AI isn’t a “prove it works” game – it’s a “help them make it work” game. Success depends less on raw model performance and far more on helping your internal champions navigate the cultural inertia. At Pascal AI Labs, we’ve seen funds that invest time learning how to work with AI – not just buy it – see the fastest ROI. Because they didn’t outsource thinking to the model; they used it to amplify their own. AI doesn’t replace capability. It compounds it. And the funds that learn to work with it - thoughtfully, contextually, and with curiosity - will move fastest and lead the industry in the decade ahead. If you are ready to make AI a compounding capability within your fund, DM me to discuss how Pascal AI Labs can partner with you.

Every enterprise wants to adopt AI but few actually integrate it. Who's seen the MIT report which says 95% of AI pilots fail? This failure is not because the tech is not good, its because it hasn't been deployed in the right manner. Many teams have discovered this the hard way. They connect an LLM to internal data, dashboards start to look smarter, workflows look automated but the enterprise itself hasn’t evolved. That gap between deploying a tool and reshaping how an organization works is what I call the **AI Enterprise Adoption Chasm** The core issue here is that most AI projects are still being framed through a productivity lens, but a generational technology like LLMs requires a complete rewiring of how enterprises think and work. The question that needs to be asked is this "What new things can we do now that were impossible before?" That’s the real test of AI maturity - when companies stop benchmarking against humans and start designing around new possibilities. But to get there, 3 things beyond the tech are needed: 1️⃣ A technology partner who can help you imagine what’s possible AND work with you to implement and adopt it 2️⃣ A willingness in the enterprise to experiment and fail 3️⃣ Investing in systems that mimic how your organization actually thinks That’s when enterprises cross the chasm - when AI stops being a side project and starts becoming an institutional capability. #enterprise #ai

🧩 The Context Engineering Problem in AI One of the biggest problems in AI right now is context. Let me break this down in terms of what we're building at Pascal AI Labs Our early customers are public and private equity investment funds. Let's say this fund went and hired the smartest person in the world. Even with that high IQ, they’d still need to understand how the fund operates - its investment philosophy, research processes, and documentation norms before they could meaningfully contribute. So this new high IQ hire would spend months grounding themselves in the fund's institutional memory: how sectors behave, how decisions are documented, how insights are shared. For a human, that could take 3, 6, 12 months - sometimes years. The mistake many teams make with AI is assuming that a generalised LLM can skip that process. Most teams we encounter buy an enterprise license, plug it in, and expect it to think like their team. Inevitably, they get stuck at simple use cases like summarizing transcripts or retrieving snippets, and then hit the valley of disappointment. The hype quickly fades because the model doesn’t understand their world. The only real way to solve this problem is to give AI context — to make it part of your fund, not just a co-pilot. That starts with two steps: 1️⃣ Teach it the domain. Horizontal models are still unreliable on financial accuracy. We’ve seen customers try using them for deep research and end up with results that are only about 70% correct — and the problem is, you never know which 70% because the answers all _sound_ smart. So first, the system needs a foundation of financial context: how industries behave, which metrics matter, where to find them, what commentary is relevant, and so on. 2️⃣ Give it your institutional memory. Just like a first-year analyst, the AI needs access to everything that defines how your fund operates — internal models, memos, meeting notes, research documents, all of it. Without that, it can’t mirror your reasoning or outputs. At Pascal AI, we work on both steps. Our system runs on top of the best horizontal models and adds the scaffolding required to understand financial context. Once we connect a fund’s internal data, the system can analyze and interpret how that fund truly operates through our proprietary knowledge graph — the institutional backbone that maps how your fund actually works. Pascal AI makes AI a first-class citizen of your fund by adding the context required for it to operate at the same level as an analyst. So when you ask the system to analyze a company, it doesn’t just look at public data. It recalls your historical notes, trades, past commentary, and how you’ve thought about that sector before. It understands your investing style and generates insights within your unique context, not from a blank slate. It's very likely that the next wave of AI won’t replace analysts. It’ll work like one - shaped by your data, your memory, and your context.

Announcement! 📢 Pascal AI Labs has been showcased as part of Inc42 Media's Startups To Watch list! We're proud to be recognised as one of the pioneers building for investment research from India to the world, and to be showcased alongside the best! Onward and upward. Mithun Madhusudan Vibhav Viswanathan https://lnkd.in/ggr7mk2d

Our co-founder & CEO Vibhav Viswanathan was in Singapore last week for SuperReturn Asia 2025, one of the premier gatherings for private markets in the region. Discussions ranged across: 🔹 AI & Deep Tech Investing, how innovation is reshaping capital allocation 🔹 The outlook for fundraising and private markets across Asia 🔹 Secondaries & liquidity as exit windows evolve 🔹 Blockchain’s emerging role in global portfolios 🔹 The continued rise of private credit and alternative financing These conversations resonate deeply with our mission at Pascal AI, building autonomous investment research workflows that bring trust, transparency, and intelligence to how decisions are made. It was an energizing week of insights and connections, and we’re excited to carry this momentum forward with our partners and clients.

Last week, our co-founder & CEO Vibhav Viswanathan 🔜 SuperReturns Asia represented Pascal AI at InvestOps Asia in Singapore, a premier gathering of leaders in investment operations, data, and technology. The conference spotlighted the challenges investment teams face today: 🔹 Ensuring data reliability & operational resilience 🔹 Scaling efficiency with automation & AI 🔹 Building secure, auditable workflows for increasingly interconnected systems These are exactly the areas Pascal AI is reimagining through autonomous investment research workflows, helping analysts move beyond manual data stitching and enabling CIOs with instant, holistic portfolio insights. Vibhav returned energized by the conversations with global peers and the strong alignment between industry needs and our mission. We’re excited to keep building with conviction toward a future where investment research empowers teams to focus on judgment, not manual work. And up next: Vibhav will be attending SuperReturn Asia 2025 — continuing to connect, learn, and share from the forefront of private markets innovation.