Seed & Early Stage · AI-Native

Backing technical founders building the next generation of AI-native software and intelligent systems.

434 VC is an early-stage venture firm investing across applied AI, infrastructure, fintech, physical AI and software transforming complex industries.

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Thesis

The value is movingto the applied layer.

Foundation models are commoditizing. The durable advantage is moving to the companies that own a specific expert workflow end to end: organizing the domain knowledge, doing the work, and improving with every cycle. The winners won't be the ones with the best model. They'll be the ones who own the workflow and the trust that comes with it.

Regulated professional services

The biggest durable opportunities are in expert work where trust, accuracy, and compliance are the product itself.

Two ways to win

We back the software firms run on, and the AI-native firms rebuilding those services from scratch.

Operator-led founders

Builders with real domain depth who lived inside the workflow first.

Moats that compound

Proprietary workflow data, regulatory trust, and deep workflow entrenchment, not a better model or a slicker UI.

Why 434 VC

We've built AIwhere mistakes matter.

We've spent the last decade building production AI in regulated, high-stakes industries, finance and healthcare especially, where trust, compliance, and accuracy aren't optional. We evaluate founders as builders who can read your architecture and product and tell you what's real, not spectators.

Goodfin (YC)Google (Search/Cloud/ML)Sprinter HealthAmazonAkamaiIntel

What We Back

Technical founders building AI-native companies.

We invest at seed and early stage across applied AI, infrastructure, fintech, physical AI, and software transforming complex industries. A particular area of strength is regulated, high-stakes work, especially finance and wealth, where we've built and have relationships.

Finance & wealth AIRegulated professional servicesAI-native firmsServices-to-softwareWorkflow automationOperator-led

Active Themes

What we're tracking right now.

Sectors describe where we invest. These are the shifts we think actually matter inside them — the questions shaping the conversations we're having with founders today.

Operator-led founders building vertical AI

The strongest seed-stage companies are built by people who lived inside the workflow before they tried to rebuild it.

Services businesses becoming software

Many enduring AI companies will begin close to the customer — through deployment or services — and evolve into scalable software over time.

Workflow data as the real moat

Foundation models are commoditizing. The durable advantage is the data captured from doing the work — labeled by experts, structured by the product, and improving with every cycle.

From copilots to agents in regulated industries

The first wave looked like assistants. The next wave actually executes work — under review, with audit trails, and against real workflows.

AI-native financial services

Wealth, fintech infrastructure, and compliance workflows are being rebuilt from the ground up around AI — not retrofitted onto legacy stacks.

Trust, accuracy, and the regulated edge

In industries where mistakes matter, the bar is not demo quality. It is production quality — explainable, auditable, and safe to ship.

A point of view, not a prediction · Updated periodically

Physical AI

AI that actsin the world.

Physical AI is one of our core investment areas. The same conviction behind our applied-AI work (AI that does real, consequential work) extends to the intelligence layer that lets machines perceive, model, decide, and operate in physical environments.

We're interested in founders building the full stack: from sensing and world models to control, robotics software, edge inference, and the data infrastructure that ties it together.

Perception & sensing

Multi-modal sensing, sensor fusion, and the data pipelines that turn raw signal into structured understanding of the physical world.

World models & simulation

Physics-aware models and simulation engines that close the loop between prediction and real-world behavior before systems are deployed.

Control & autonomy

Decision-making under uncertainty, planning, and control systems that operate safely and reliably without constant human intervention.

Robotics & embodied systems

Software-defined machines — industrial, mobile, and human-scale — where AI is the core competence, not a bolt-on feature.

Edge AI & compute

Efficient inference, on-device intelligence, and the compute infrastructure that makes real-time action feasible where bandwidth and latency matter.

Data & infrastructure

The data layer, tooling, and platforms that collect, label, curate, and govern physical-world data at scale.

434-backed · Case study

Ottonomy

Intelligence that has to work beyond the demo.

Ottonomy builds autonomous delivery robots for demanding indoor and outdoor environments. It is a clear expression of what we look for in physical AI: software, hardware, and operations designed as one system.

Visit Ottonomy

Why we invested

The hard part of autonomy is not making a machine move once. It is making the full system perceive, decide, and act reliably in environments that refuse to stay controlled.

At Google, we learned what separates an impressive model from a production machine-learning system: feedback loops, infrastructure, edge cases, and relentless attention to reliability. Physical AI raises that bar. Errors no longer live only on a screen; they meet people, objects, weather, latency, and changing terrain.

Nikhil Tyagi brings the complementary systems lens. Across Broadcom, Apple, Tesla, Meta, and Qualcomm, he has spent two decades taking connectivity from silicon to shipped products. That experience sharpens the question behind this investment: can every layer work together under real-world constraints, not just in isolation?

Ottonomy fits that conviction. Its robots bring perception, planning, control, edge intelligence, and fleet operations into one deployed system. Each real-world run can create the operational data needed to improve the next one. That is the kind of compounding loop we want to back.

Investment signals

Full-stack autonomy: Perception, planning, control, and operations built together.
Real deployment: A system designed for dynamic indoor and outdoor environments.
Edge reliability: Intelligence that must perform where latency and connectivity matter.
Data that compounds: Operational experience that can strengthen the deployed system over time.

What You Get

More than a check.

434 VC is built for founders who want a technical partner with conviction about the build, the workflow, and the domain — not a passive line on the cap table.

A Technical Partner Who's Shipped

We read your architecture, model choices, and product the way a builder who's shipped would. Honest reads, sharper questions.

Buyer & Design-Partner Relationships

We have relationships with buyers and operators across finance, wealth, and legal. Where we can open a door for a design partner or first customer, we will. Where we can't, we'll say so.

Founder Community & Deal Flow

Through House of 434, YC, and our operator network, you plug into the rooms where this work is already happening.

Invested Like a Builder

We've written checks and sat on the founder side. We know what helps early and what's noise.

How We Invest

Stage, check size, and what to expect.

We invest at seed and early stage in technical founders building AI-native software and intelligent systems, typically as a lead or meaningful co-investor. We lead with technical conviction about the team and the build. Fund I is being raised now and actively making investments.

We review every submission and will reach out if there's a potential fit.

How We Source

How we source.

Our deal flow is earned, not paid for. Through House of 434, the community we convene, plus the YC network as founders ourselves and a deep bench of ex-Google operators, we see strong technical founders early, often before they're raising.

Founder-Market Fit

The signal we weightmost heavily.

At the early stage, the single most important signal is founder-market fit: domain depth that goes beyond a deck, and conviction earned by living inside the workflow you're rebuilding.

We back founders who understand the buyer, the workflow, and the constraints of the industry better than anyone else trying to serve it.

Are We a Fit

A quick self-check.

Not every great company is a 434 VC company. The more of these that describe you, the more likely there's something for us to talk about.

For Founders

0 / 7

What gets us to pass

✕AI wrapper without a moat
✕No founder-market fit
✕Unclear buyer
✕A services business with no credible software path
✕A team that can demo but can't ship to production
✕No path to venture-scale outcomes
✕Technology searching for a problem

These are not negotiable.

Why 434

434 began as an address. 434 Bayview was the home where many of the most important chapters of our story began. Looking back, what stood out was not the house. It was what emerged from it. Conversations that became friendships. Friendships that became collaborations. Collaborations that became companies.

434 VC is built on that belief. That meaningful things often start small. That the highest-signal ideas and the strongest relationships live in private rooms long before they become visible.

434 VC exists to back founders building the future of software through AI.

Founder

Founder & Managing Partner

Shilpi Nayak

Founder & Managing Partner, 434 VC

Shilpi Nayak is the Founder and Managing Partner of 434 VC.

She has spent the last decade building production AI in the industries where it's hardest to get right — finance, healthcare, and other regulated, high-stakes domains. As Co-Founder and CTO of Goodfin (YC), she built the AI platform from the ground up and co-led the launch of Goodfin Go, the system that opened access to the most sought-after private companies of this cycle — SpaceX, Anthropic, xAI, Cerebras — to a new generation of investors. Before that she spent four years at Google building large-scale machine learning across Search, Cloud, and user modeling, with earlier engineering work at Amazon, Akamai, and Intel.

Her work on AI in financial analysis has been featured in the Financial Times and CNBC. She has spoken at Money 20/20 and New York Tech Week, and has been an active angel investor for years, backing AI-native founders including Thomas.

She started 434 VC to back the operators she knows best: builders turning hard-won domain expertise into the next generation of AI-native companies.

House of 434

A community for founders, operators, and investors.

House of 434 is where founders, operators, and investors explore AI, capital, and company building together — intimate conversations, field notes, and gatherings around the questions shaping the applied-AI era.

House of 434 and 434 VC are separate: the community stands on its own, and we're the common thread behind both.

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Contact

Building something we should know about?

We're always interested in meeting exceptional technical founders building ambitious companies across our areas of focus.

We review every submission and will reach out if there's a potential fit.

Submit Your Company