Over the past decade, we have seen artificial intelligence move from centralized cloud environments to the very edge of the network—onto machines, sensors, vehicles, and embedded devices. This shift is not merely a technology evolution; it represents a fundamental change in how businesses generate value from data.

• Edge AI is no longer about proofs of concept. Enterprises today are asking harder questions:

• How do we deploy intelligence at scale?
• How do we reduce dependency on scarce AI talent?
• How do we achieve measurable business outcomes—faster, more reliably, and at lower cost?

• At Meritech, we believe the answer lies in rethinking how edge intelligence is built and deployed.

Why Traditional Edge AI Development Is Not Scalable

Most organizations quickly discover that traditional approaches of edge AI development to solve business problems do not scale well. Building production-grade solutions typically requires deep expertise across embedded systems, machine learning, data engineering, and hardware optimization. This creates long development cycles, high costs, and heavy dependence on a small pool of specialists.

At the same time, business teams—those closest to operations, quality, and customers—often remain disconnected from the development process of building solutions, despite being the ones who best understand the problem.

This disconnect slows innovation and limits business impact on solving business problems.

Low-Code as a Strategic Enabler for Edge AI

Low-code/No-code platforms represent a structural shift in how edge AI technology is consumed inside enterprises. They abstract complexity, standardize workflows, and enable faster decision-making without compromising reliability.

When applied to edge AI, a low-code/no-code platform becomes a strategic enabler, not just a productivity tool. It allows organizations to:

 

• Abstract business problems quickly
• Standardise edge AI development workflows
• Move from pilots to production faster
• Empower domain experts, not just specialists
• Deploy intelligence closer to the source of data
• Reduce long-term operational and cloud costs

This belief is what led us to invest deeply in building a purpose-built low-code/no-code platform for edge intelligence that brings end-to-end AI/ML model lifecycle - all in one place.

eFabric: A Flagship Platform Built for Real-World Edge AI

eFabric is the flagship platform developed by Eoxys, a subsidiary of Meritech.

Eoxys was given a clear mandate: to build a platform that bridges the gap between advanced edge AI capabilities and real-world enterprise adoption. eFabric is the outcome of that mandate.

The platform is designed to cover the entire lifecycle of edge AI —from data ingestion and model development to optimization and deployment—through an intuitive, low-code/no-code interface. The intent is simple: enable organizations to focus on business outcomes, not infrastructure complexity.

Where We See Immediate Business Value

Across industries, we see consistent patterns where edge intelligence delivers tangible impact:

• Audio Classification

  Edge–based audio classification—such as wake-word detection and sound pattern recognition (e.g., glass break, baby cry, human voice)—is seeing rapid adoption across multiple sectors. By enabling real-time, on-device inference, these capabilities support low-latency response, improved privacy, and reduced reliance on cloud connectivity.

• Predictive Maintenance and Asset Reliability

  Edge-based anomaly detection on sensor time-series data allows failures to be identified early—before they become costly incidents. Running intelligence locally ensures low latency, high availability, and reduced dependence on cloud connectivity.

• Manufacturing Quality and Embedded Vision

  Real-time video based inspection and image classification at the edge improve throughput and reduce waste. eFabric enables teams to build, train and deploy optimized vision models directly on embedded hardware without prolonged development cycles.

• Smart Infrastructure and Environmental Intelligence

  From buildings to utilities, continuous local monitoring improves efficiency, compliance, and responsiveness—while keeping sensitive data on-device.
  
  What is common across these use cases is not the model sophistication, but the ability to deploy, manage, and scale intelligence reliably. That is where platforms matter.

A Platform Strategy, Not a Point Solution

Our philosophy with eFabric is clear: enterprises do not need more fragmented tools. They need platforms that standardize and scale.

By adopting a low-code/no-code edge AI platform, organizations gain:

• Faster time-to-value
• Lower total cost of ownership
• Improved governance and consistency
• Better collaboration between business and engineering teams

This becomes especially important as edge deployments grow from tens of thousands of devices to millions.

Looking Ahead

Edge AI has entered its execution phase. The winners will not be those with the most experimental models, but those who can operationalize intelligence at scale—from AI model to silicon.

At Meritech, through Eoxys and eFabric, our focus is on enabling this transition—helping enterprises move from isolated innovation to sustained, measurable impact.

Low-code/No-code platforms are not about simplifying ambition. They are about removing friction across the entire stack—from AI model to silicon so that ambition can be realized faster and more reliably.

That, in our view, is the future of edge intelligence.