Ambient Point builds quantitative solutions powered by AI — as advisor, builder, and operator. We help organizations decide where analytics and AI belong in their most consequential decisions, build the models and systems that put them there, and run companies where people, AI agents, and data operate as one team. With domain experts as partners, we encode their judgment into quantitative systems they can trust — co-designing, co-building, and co-owning the outcome.
View our portfolioModels are capable; organizations aren't structured to turn their output into action. Expertise lives in people's heads, forecasts stop short of decisions, and no one owns the handoff between judgment and automation. We focus on operational and financial domains where that gap is widest — and build AI-powered quantitative solutions that close it.
AI is only as good as what it's taught. In each domain we build operator-level fluency, then encode how practitioners actually work and decide into quantitative models — forecasts, valuations, optimization, and guardrails that make AI-driven judgment legible, testable, and improvable.
We design the decision layer: how AI models, optimization engines, people, and agents divide work, hand off, escalate, and verify. This is where AI stops being a demo and becomes an operation — explicit objectives, constraints, and control points that let a small expert team run at scale.
We build and run the companies that put this to work. We co-create AI-powered ventures with founding teams and enterprise partners, taking equity positions alongside operational responsibility. Our principals hold direct, hands-on leadership roles charged with advancing product roadmaps, establishing client relationships, and activating revenue.
We combine quantitative rigor, AI engineering, domain depth, and hands-on operating leadership — a model that sets us apart from traditional consultancies, systems integrators, and venture funds.
Our expert network includes practitioners with decades of experience leading quantitative, data science, and AI programs at major financial institutions, enterprise technology companies, and high-growth analytics firms. That operational depth — knowing how decisions actually get made, and where AI improves them — shapes every engagement we take on.
In venture partnerships, we take equity positions rather than relying solely on fees. Our economics are aligned with the long-term success of each venture, not the length of the engagement.
We concentrate on a few domains — quantitative finance, risk and operational intelligence above all — because a model only earns trust with expertise specific to how decisions get made there. That is what turns a capable model into a trusted system.
Each portfolio venture runs as a human-AI operating team: dedicated engineering, revenue, and domain leadership, working alongside AI agents with defined roles and clear accountability. When we build a company, we mean an organization with the people, systems, and client relationships to sustain it.
Every venture reflects the same thesis: a domain where expert judgment can be quantified and scaled with AI, run by a team where people and AI share the work.
Quantome develops quantitative solutions — from powerful analytics and innovative data products to automated workflows and market intelligence — for the specialty credit and structured finance markets. The platform turns fragmented, opaque credit data into standardized, signal-rich data products.
Visit QuantomeInRiva AI builds agentic systems that monitor operational lifecycles in real time across the supply chain — reading every state transition, learning what normal looks like at each stage, and acting the moment reality drifts in inventory management, fulfillment, logistics, field service, and customer operations.
Visit InRiva AIFind the gap. We target domains where valuable decisions are made one at a time by experienced people, never connected to the data and AI that could carry them further. Encode. We work alongside those experts to capture how they judge and act, and build quantitative models that make that judgment reliable, auditable, and repeatable. Optimize. We design how models, people, and AI agents divide the work — who decides, who executes, who verifies — so expertise turns into action at scale. Operate. Our principals serve in operating roles at every portfolio company, not as outside advisors.
Ariel Rodriguez is an analytics technology executive with over 25 years of experience in data science, AI/ML solutions, structured finance and financial analytics. He founded Ambient Point Corp. to build and operate ventures that turn expert judgment into AI-powered quantitative solutions at scale.
Ariel was previously the Head of Real-Time Analytics Solutions at JPMorganChase, where he led data science teams developing real-time data products and AI/ML solutions across all banking business lines. That work improved customer experiences and operational efficiency through real-time audience targeting, digital journey engagement analytics, ML-optimized back-office processes, and AI-guided customer service interactions.
He has also held leadership roles at high growth technology companies, including: Chief Data Scientist at Scuba Analytics, a real-time analytics platform used by top-tier clients including Microsoft, Salesforce, Warner Bros. Discovery, and Comcast; Head of Data Science at Jornaya, a customer journey data company, now part of Verisk; and Product Leader of the Behavior Change Analytics Platform at Mobiquity, a digital consultancy and cloud solutions provider, acquired by Hexaware.
Earlier in his career, Ariel built deep expertise in structured finance and predictive sciences. At Luminent Mortgage Capital, a mortgage REIT with a $15 billion portfolio of securitized mortgage loans, residual interests and asset-backed securities, he led whole-loan trading, prepayment and credit risk modeling, and MBS structuring analytics. He later served as a special situations advisor to credit funds, valuing distressed mortgage-backed and asset-backed securities holdings and executing loss mitigation strategies. At JPMorganChase Asset-Backed Capital Markets, Advanta, and TD Bank, he developed predictive models for prepayment, delinquency and loss forecasting, credit and behavioral scoring, and real-time fraud detection.
Ariel is a graduate of Stanford University and the University of Puerto Rico, and is an INFORMS Certified Analytics Professional, and a HITRUST Certified De-Identification Professional.
Whether you're exploring a venture partnership, need senior quantitative and AI leadership for a critical initiative, or are working out how AI should inform your most important decisions — we welcome the conversation.
We engage in three ways: venture partnerships where we co-build and take equity, embedded operator roles where we lead critical quantitative and AI initiatives, and focused advisory engagements scoped to a specific decision or model. Every engagement starts with a conversation.
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