Nevoya, the enterprise AI startup founded by Sami Khan, has just closed a $9.3 million seed round to bring a new generation of intelligent agents to market - ones that don’t just generate text, but actually understand their environment, reason across workflows, and execute with clarity.

The round, led by a powerhouse syndicate including Lowercarbon Capital, Floating Point, LMNT Ventures, Third Sphere, Stepchange, Never Lift, and Qasar Younis, was quietly announced - yet insiders and early adopters are buzzing. Many believe Nevoya might be one of the most promising foundational bets in enterprise AI this year

In a sea of LLM wrappers, Nevoya stands out by focusing on what enterprise users desperately need: context-aware intelligence that integrates directly into business logic, tools, and day-to-day decision-making. Not just smart chatbots, but agents that know where they are, what matters, and what should happen next.

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Beyond Co-Pilots: Nevoya’s Full-Stack Context Engine

While most AI startups still play in the “assistant” zone - summarizing documents, replying to emails, or suggesting replies - Nevoya is going after the hard stuff: complex decision flows in high-stakes business environments.

Their approach is built on a novel context engine that captures and maintains a persistent awareness of:

• Business objectives
  
• Toolchain architectures
  
• Historical decisions
  
• Role-specific priorities
  
• External events and real-time updates
  

This is far more than prompt chaining or vector databases. Nevoya is designing AI agents that function with stateful memory, logic reasoning, and vertical-specific ontologies.

The result? AI that can move from summarizing meetings to proposing follow-ups, scheduling resources, escalating blockers, and even handling multi-department coordination - all without losing context or hallucinating decisions.

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What Most Founders Get Wrong About Agentic AI

A critical insight that Nevoya has hardcoded into their thesis:
Agent intelligence isn’t about size - it’s about context fidelity.

While the broader AI world chases ever-larger models and marginal gains in token throughput, Nevoya is doubling down on environmental accuracy and behavioral relevance.

Why does this matter? Because real-world enterprise problems aren’t about guessing the next word - they’re about knowing the next best action. And that means knowing the why, when, and how, not just the what.

Here’s the insight most founders miss:

"In enterprise use cases, a smaller agent that deeply understands your tools, priorities, and workflows will outperform a bigger model that doesn’t know your world."

That’s the race Nevoya is winning - not by being louder, but by being sharper.

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Why Investors Are Betting Big on Nevoya

Nevoya’s round may be under the radar, but the names backing this venture clearly believe in the long game. While many AI startups are rushing to demo flashy outputs, Nevoya is focusing on sustained operational value, deep integrations, and long-term trust.

Their agents are already being tested in finance, logistics, and compliance-heavy verticals where accuracy and reasoning trump glitzy interfaces.

With deep R&D and a clear architectural moat, Nevoya’s stack includes:

• Modular agents tailored for domain-specific tasks
  
• Memory architecture that adapts across user sessions
  
• Integration layers with enterprise data lakes, APIs, and compliance protocols
  
• Explainability and audit trails baked into every action
  

It’s a stack built for serious work, not one-off demos. And that’s precisely why enterprise buyers - and investors - are leaning in.

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Founder Spotlight: Sami Khan

Sami Khan isn’t new to building high-conviction tech. Before Nevoya, he co-founded Lucid, which scaled across sectors by blending clean product design with strong technical infrastructure. He brings the same ethos here: ambitious architecture, but hyper-practical in execution.

Khan’s focus is on first principles AI - thinking from the ground up about what makes an agent useful, trustworthy, and adoptable in enterprise environments. This isn’t about plugging in ChatGPT. It’s about reimagining decision augmentation in a world where AI isn’t just present - it’s accountable.

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The Market Is Hungry for Meaningful AI Agents

The generative AI market is projected to reach $1.3 trillion by 2032 (Bloomberg Intelligence), but within that, there’s a growing demand for verticalized, purpose-built AI agents.

According to Gartner, by 2026:

• 30% of enterprise teams will be supported by autonomous agents
  
• 70% of AI deployments will be domain-specific rather than general-purpose
  
• 50% of business tasks previously done manually will involve AI decision logic
  

But here’s the kicker: enterprises don't want agents that just talk. They want agents that think, act, and align.

Nevoya is building exactly that - fitting into the growing trend of contextualized, integrated intelligence that elevates enterprise ops instead of disrupting them.

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What’s Next for Nevoya

Armed with its seed funding, Nevoya plans to:

• Expand its context engine into new verticals: including manufacturing, fintech, and customer ops
  
• Launch APIs for enterprises to deploy tailored agents inside existing toolchains
  
• Deepen partnerships with compliance and data governance tools
  
• Hire for roles in reasoning architecture, NLP, and B2B growth
  

They’re also quietly building a developer-facing agent SDK, allowing companies to build internal AI teammates that function with real autonomy - and real accountability.

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Final Take: Nevoya Is the Quiet AI Powerhouse to Watch

In an AI world obsessed with showing off what’s flashy, Nevoya is focused on what works.

This $9.3M seed round isn’t just fuel - it’s a commitment to building the backbone of AI-driven enterprise decision-making. Their bet? That context will win over capability - and that reasoning beats regurgitation in the long run.

Founders building in this space should take note:
Don’t just build agents that complete prompts.
Build agents that complete workflows.
That’s what Nevoya is doing - and that’s why they’ll matter.