Democratizing

Democratizing

the AI Epoch

the AI Epoch

By fusing Generative and Predictive AI through a first-principles architectural moat, we compress months of ML development into days of measurable business impact. We don't just process data; we empower the mid-market with radically context-aware agents that out-innovate the enterprise without compromising data sovereignty.

Discover more

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Our Mission

To level the playing field in the AI epoch by building high-impact, first-principles technology that empowers non-enterprise businesses to out-compete and out-innovate.



The Problem

Mid-market businesses are ready to embrace AI, but 87% of ML initiatives never reach production. Why?

Businesses are ready to embrace AI, but most AI initiatives fail to create significant company-level impact. Why?

Because generic "wrapper" tools fail to capture the deep, unique context of a specific business, leaving leaders with expensive, "smart" tech that doesn't move the P&L. Expert AI and Data Science talent is scarce and prohibitively expensive for most organizations. Infrastructure costs are climbing while measurable ROI remains elusive.Growing concerns over data privacy and security often freeze innovation before it even starts.

Because most AI today doesn’t capture the context, is slow to adapt, and is expensive. At the same time, businesses are concerned about sharing their sensitive data with AI vendors. Also, access to deep AI is gated. Expert AI and Data Science talent is scarce, and limited to technically advanced organizations such as Tesla or Google.

Our Solution

We are uniting the advancements in Generative AI and Predictive AI to deliver systems-level impact.

To innovate these solutions, we will follow human-centered design methodologies and a first principles approach. 

To solve these challenges, we are following the following tenets:

A "10x" Architectural Moat

By combining multi-agent systems with cutting-edge modeling techniques such as Liquid Neural Networks, we will achieve superior performance with a fraction of the compute costs and time to value, reducing the whole initiative timeline from months to days.

Radical Contextual Awareness:

We go beyond static data. Our innovative context agents augment business stakeholders by learning the nuances of your specific industry and market in real-time and continually learning to improve the output.

Zero-Risk Data Sovereignty

To solve the privacy crisis, we are pioneering ways to deliver high-accuracy predictions based solely on metadata, ensuring your customer data promise remains intact.

Native AI Architecture

We are uniting the advancements in Generative AI and Predictive AI to deliver systems-level impact. By combining multi-agent systems with cutting-edge modeling techniques such as Liquid Neural Networks, we will achieve superior performance with a fraction of the compute costs and time to value, reducing the whole initiative timeline from months to days.

Context is king

We go beyond static data. Our innovative context agents augment business stakeholders by learning the nuances of your specific industry, your company and market in real-time and continually learning to improve the output.

Human centered design, first principles approach

We will start with the users and customers first, instead of developing technology solutions and then looking for a problem. We will explore and solve the root causes of the challenges

Our Team

Metricsamp.ai is powered by people who bring different skills but share the same commitment to clarity and quality.

Our team is small but highly organized, working seamlessly across branding, design, and development.

Deudutta Nagori

AI/ML Engineering, Data Science, Autonomous Vehicles, Semiconductors

Deudutta Nagori

Co-founder and CEO

Mohit Manwani

AI/ML Platform Engineering, Fintech

Mohit Manwani

Founding AI Engineer

Raveesh Budania

Tech Product Management, Corporate Innovation, Edge AI

Raveesh Budania

Co-founder and Chief Product Officer

Mohit Guleria

AI/ML Engineering, Fintech

Mohit Guleria

Founding AI Engineer

Deudutta engineered AI for Google’s Tensor as well as Tesla’s Autopilot and Dojo Supercomputer for 8 years. Previously he has also worked in R&D at Samsung and Intel. Leveraging his deep AI engineering expertise, he will develop AI solutions that are more evenly distributed, contextual and resource-efficient. He holds an MS in electrical and computer architecture engineering from Arizona State University and an MBA from UC Berkeley’s Haas School of Business.

Raveesh has been a product leader within the corporate innovation space. He has delivered Edge AI innovations at Dell’s R&D arm, and helped Amazon and Maersk create new business lines. He holds an MBA from the University of Texas at Austin. This is not his first rodeo; he has started and scaled businesses in the past.

Mohit Manwani has engineered large-scale AI systems at Visa for more than 4 years. He holds a Master’s degree in Computer Science from Indian Institute of Information Technology, Hyderabad.

Mohit Guleria leads the development of AI applications at MetricsAmp. He holds a Bachelor’s of Technology degree from Indian Institute of Technology, Roorkee, and worked as an engineer at Visa.