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Stealth AI Startup

Trust-driven AI lifecycle management platform

Overview

Neuromorphic Labs builds an enterprise platform that acts as a trust layer for AI, helping organizations move from using AI as a service to owning it as strategic infrastructure. It provides visibility, control, and verifiability across the AI lifecycle by tracking every change to models, data, and agents to ensure provenance, consistency, and policy enforcement during runtime. The platform integrates with tools data scientists already use and works across public clouds, on-premises systems, and edge infrastructure. Unlike competitors that offer isolated tools, Neuromorphic Labs focuses on built‑in trust and governance across the entire AI lifecycle, from development to deployment. Its goal is to make building, deploying, and operating AI as seamless and reliable as modern software development, with trust embedded at every stage.

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About Stealth AI Startup

Simplify's Rating
Why Stealth AI Startup is rated
C
Rated C on Competitive Edge
Rated C on Growth Potential
Rated C on Differentiation

Industries

Data & AnalyticsEnterprise SoftwareCybersecurityAI & Machine Learning

Company Size

1-10

Company Stage

Seed

Total Funding

$5.1M

Headquarters

Seattle, WA

Founded

2025

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Simplify's take

What believers are saying

  • August 2026 seed funding raised $5.1 million from Flying Fish, Toyota Ventures, and Amplify.
  • The October 2026 site update signals active product development toward general availability.
  • Enterprises now treat AI provenance and policy enforcement as core infrastructure, not optional compliance.

What critics are saying

  • Gartner counted 100-plus AI governance vendors and 13 leaders in June 2026.
  • IBM and ModelOp keep bundling governance into broader platforms, squeezing startup budgets.
  • No public customers or revenue are disclosed; missed adoption before general availability burns the seed round.

What makes Stealth AI Startup unique

  • Unique cryptographic identities track every dataset, model, agent, policy, and compute environment.
  • Chetan Kapoor combines AWS AI infrastructure scale and CoreWeave product leadership.
  • It runs in customer environments across cloud, on-premises, and edge systems.

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Funding

Total Funding

$5.1M

Above

Industry Average

Funded Over

1 Rounds

Seed funding is usually the first official round after pre-seed, when a startup has a prototype or concept. It’s typically used to develop the product, test the market, and start building the team. Investors here are often angel investors or early-stage venture capitalists.
Seed Funding Comparison
Above Average

Industry standards

$3.3M
$2M
$2.3M
$3M
$5.1M

Benefits

  • Company Equity

Company News

EIN News
Aug 13th, 2026
Neuromorphic Labs raises $5.1M to build trust layer for production AI systems

Neuromorphic Labs has raised $5.1 million in an oversubscribed seed round led by Flying Fish, with participation from Toyota Ventures, Amplify Partners, Tandem Ventures, SaaS Ventures and others. The Seattle-based company is building a trust layer for production AI, enabling organisations to develop, deploy and integrate models and agents into business-critical applications with control and verifiability. The platform embeds trust into the AI lifecycle by giving models, weights, datasets and agents verifiable identity, tracking them through every change to enable provenance and policy enforcement at runtime. It works with existing tools and runs across public clouds, on-premises, edge and hybrid infrastructure. Founder and chief executive Chetan Kapoor previously served as chief product officer at CoreWeave and led the AI infrastructure business at AWS.

Cadence
Mar 12th, 2026
Welcome to the Cadence blog.

Welcome to the Cadence blog. Cadence Team · March 12, 2026 Cadence is excited to launch the Cadence blog. This is where Cadence'll be sharing its thinking on AI-assisted development, engineering team productivity, and the tools and practices that help dev teams ship better software, faster. What to expect. Cadence'll be writing about: * AI coding insights - what Cadence is learning about how developers actually use AI tools, and what separates the teams that get real value from the ones that don't. * Product updates - new features, improvements, and the reasoning behind its product decisions. * Engineering culture - how high-performing teams build habits around visibility, feedback, and continuous improvement. Why a blog? Cadence talk to engineering leaders every day who are navigating the same questions: How do I know if my team is getting value from AI coding tools? How do I help them improve? What does "good" even look like? Cadence want to share what Cadence is learning - from its own product data, from conversations with customers, and from building Cadence itself. Stay tuned. Cadence has a lot to share.

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