NovaRize.ai’s cover photo
NovaRize.ai

NovaRize.ai

IT Services and IT Consulting

AI Strategy and Engineering for Financial Services

About us

AI-native enterprise service firm helping asset managers, hedge funds, and PE firms move from AI exploration to production-grade systems — with a focus on agentic AI architectures, quantitative signal extraction, and data platform modernization (Snowflake, Databricks, AWS). Focus areas: → Agentic AI systems for investment research and operations → NLP/LLM pipelines for regulatory, earnings, and central bank signal extraction → Data architecture advisory (medallion architectures, Snowflake Cortex, cloud-native) → AI strategy and roadmap development for CIOs and COOs

Website
https://www.novarize.ai
Industry
IT Services and IT Consulting
Company size
2-10 employees
Headquarters
New York City
Type
Privately Held
Founded
2026
Specialties
AI, Data, Analytics, Strategy, Financial Services, Asset Management, Hedge Funds, Private Equity, and Capital Markets

Locations

Employees at NovaRize.ai

Updates

  • The distinction between Individual AI and Enterprise AI is one most firms haven't fully worked through yet. J-G Prince breaks it down in his latest post: individual tools give analysts a 20–37% productivity lift on bounded tasks. Valuable. But firm-level capability requires something different — a knowledge layer that compounds across the whole team, persists when people leave, and runs on verified data. The electrification analogy he borrows from George Sivulka at Andreessen Horowitz is the right frame: most firms have swapped the motor. Few have redesigned the factory floor. Worth reading if you lead operations, investments, or technology at an asset manager or wealth management firm. https://lnkd.in/ersSTmq9

    View profile for J-G Prince

    Most firms I talk to have Individual AI. Almost none have Enterprise AI (yet). Individual AI is what happens when analysts use Copilot, Claude, or ChatGPT on their own. The research is clear on what it produces: 20–37% faster on bounded tasks: drafting, summarizing, retrieving. Real gains. Worth having. What it doesn't produce is firm-level capability. George Sivulka wrote about this for Andreessen Horowitz recently (see link in comments). The analogy he uses is electrification. When factories first got electric power, most just swapped the steam engine for an electric motor and left the floor plan unchanged. Productivity barely moved. The gains came later — only when factories were redesigned around what electricity actually made possible. We've swapped the motor. Most firms haven't touched the factory floor. Individual AI augments people. Enterprise AI encodes the firm. It's a knowledge layer built at the firm level: research that compounds across the whole team, institutional memory that doesn't leave when a senior person does, processes running on verified firm data with a full audit trail. What I keep seeing in recent conversations, across wealth managers, real estate investors, and asset managers ranging from $1B to over $1 trillion in AUM, is that the firm knowledge layer has gone from a nice-to-have to the thing everyone is trying to figure out. Mastering this will be how some asset managers will go from having 10-20% increase in individual productivity (individual AI) to increasing their AUM 5-10X without a proportional headcount increase (enterprise AI). More on this in my upcoming posts.

    • No alternative text description for this image
  • Most AI business cases in financial services calculate one thing: labor savings. That's the floor, not the ceiling. Our founder J-G breaks down the full framework — five scoring dimensions, three dollar-value channels (labor savings, risk avoidance, revenue uplift), and the industry multipliers that typically push the real number 2-3x beyond the initial estimate. Read the full piece → https://lnkd.in/ew-pXaQQ Run it on your own project → nvs.novarize.ai

  • Every AI project in financial services hits the same wall: The technology works. The POC looks great. Then someone asks what it's worth — and nobody has a defensible answer. We built NVS™ to fix that. It's a free, proprietary, AI-powered tool that runs a structured intake on any AI project and outputs a composite score, tier classification, and annualized business value in dollars — broken into labor savings, risk avoidance, and revenue uplift. 50 cited research articles. 11 questions. 3 minutes. Watch how it works 🚀 and try it for yourself! Link in first comment ⬇️

  • J-G Prince breaks down why financial services AI projects are routinely undercounted — and how to fix the math. Try NVS™: nvs.novarize.ai https://lnkd.in/ekaazTVB

    View profile for J-G Prince

    Most AI projects in financial services never make it to production. Not because the technology doesn't work. Because nobody could put a dollar figure on the value. I've seen this from both sides — as a buy-side quant and now working with AM and PE firms on production AI. The pattern is always the same: 1) A team builds a working proof of concept 2) Leadership asks "what's this worth?" 3) Nobody has a defensible answer 4) Budget for scaling and production deployment doesn't get approved McKinsey estimates 63% of AI value in knowledge work comes from sources other than direct labor savings. Most firms calculate only the labor savings. And massively undercount the business case. I built a tool for this. More on that this week. ⬇️ Link in first comment #AIinFinance #FinancialServices #AIStrategy

    • No alternative text description for this image
  • This week, our founder J-G Prince attended Open Data Science Conference (ODSC) in Boston — including an executive dinner he organized with AI leaders from top asset managers and banks. He shared his key takeaways on his personal page (worth a read). A few things stood out to us as a firm: The MLOps parallel is the right one. A decade ago, the industry discovered that building ML models was the easy part — it was the deployment, monitoring, and maintenance infrastructure that determined whether value actually landed. We're at that same inflection point with agentic AI. The firms that get ahead of it now won't be scrambling to retrofit governance and evaluation frameworks in 12 months. The ROI gap is real and specific. Most organizations are measuring whether their AI answers questions correctly. That's not ROI. The firms we work with that are seeing real returns have moved beyond Q&A — their AI surfaces strategic insight, thinks ahead of the question, and changes how decisions get made. That's a different design challenge, and it requires a different kind of thought leadership. The governance gap is an exam risk, not just an operational one. Most firms have an AI policy. Far fewer enforce compliance. Almost none automate the checks. In financial services, that's not an abstract concern — it's where regulatory exposure quietly accumulates. This is exactly the terrain NovaRize operates in — helping financial services firms move from pilot to production with the governance, data infrastructure, and evaluation frameworks that make it last. Read J-G's full post for the on-the-ground perspective from three days of sessions and conversations here: https://lnkd.in/e3bi5c5K #ODSC #ArtificialIntelligence #FinancialServices #AIStrategy #EnterpriseAI #AssetManagement #NovaRize

    • No alternative text description for this image
  • View organization page for NovaRize.ai

    42 followers

    NovaRize.ai is in Boston this week for Open Data Science Conference (ODSC)! Our founder J-G Prince will be hosting two private executive  dinners alongside the conference for hand-picked AI leaders from financial  services. At the AI X Leadership Summit, we're particularly watching the  "Technology to ROI — AI in Finance" panel (Barbara Widholm,  State Street · Preethi Raghavan, Fidelity and Hema Retty, PhD, MBA, Managing Director of AI at BlackRock) and the enterprise AI scaling roundtables. NovaRize works at the intersection of Enterprise AI and financial services. If your firm is figuring out how AI can add value at the enterprise/fund level — not just the individual/analyst level — from front to back office, we'd like to talk. #ODSC #AIinFinance #AssetManagement #InstitutionalAI

    • No alternative text description for this image

Similar pages