Blog

GenAI Protos was born out of a mission to solve real-world challenges with innovative Generative AI solutions.

Jan 16, 2026

How to Prioritize AI Use Cases for Maximum Business Impact

Identifying the right AI use cases is critical to a successful AI strategy. Rather than pursuing every AI idea, organizations must focus on initiatives that directly drive business outcomes such as growth, efficiency, and customer satisfaction. By aligning AI use cases with strategic goals and balancing impact against effort, companies can maximize ROI and scale AI effectively. Align AI Strategy with Business Objectives

Alex Chen

Documentation at Scale: How Generative AI Eliminates the Knowledge Gaps in Your Data Program

Data Engineering

Jan 15, 2026

Automate Data Docs & Eliminate Knowledge Gaps

In enterprise data teams, documentation is the first thing to be deprioritized – and the first thing you wish you had later. You’ve probably felt the pain: onboarding new engineers takes weeks, legacy pipelines are poorly understood, and code reviews turn into guessing games. When documentation is missing or outdated, progress slows across the board. At GenAI Protos, we use Generative AI to make documentation fast, automatic, and consistent – at scale.

Michael Connel

Multi Agent AI

Jan 14, 2026

Building Scalable Multi-Agent AI Systems for Enterprise Workflows

Modern AI applications are moving beyond single-model systems. As enterprise workflows grow more complex, multi-agent AI has emerged as a practical and scalable approach to automation. Instead of relying on one generalized model, a multi-agent architecture distributes intelligence across multiple AI agents, each specialized for a specific task. These agents communicate, share context, and operate in parallel to deliver faster, more reliable outcomes.

David Martinez

Edge AI Trends and Use Cases - Why Real Time Inferencing Matters

Edge AIEmbedded AI

Jan 13, 2026

Edge AI Trends and Use Cases: Why Real-Time Inference Matters

Edge AI combines artificial intelligence with edge computing, running models directly on local devices or IoT sensors. This on-device processing enables real-time inference - analysis and decision-making in milliseconds - without constant reliance on the cloud. For example, self-driving cars, wearables, security cameras and smart appliances process data locally to deliver immediate insights when milliseconds count. By handling data at the source, Edge AI powers low-latency real-time analytics and preserves privacy, since sensitive information can stay on-device.

Alex Chen

Comparison table of top Edge AI devices including Jetson Nano, Google Coral, Raspberry Pi 4, Intel Neural Compute Stick 2, and BeagleBone AI-64 showing AI power, best use cases, strengths, and limitations.

Artificial IntelligenceEdge AI

Jan 12, 2026

Top 5 Edge AI Devices Like NVIDIA Jetson Nano

Top 5 Edge AI devices for 2025 that enable on-device machine learning and real-time decision-making without constant cloud dependence. It ranks popular platforms like NVIDIA Jetson Nano, Google Coral Dev Board, Raspberry Pi 4 with AI accelerators, Intel Neural Compute Stick 2, and BeagleBone AI-64, detailing their best use cases, strengths, and limitations for vision, robotics, IoT and industrial applications. These devices help developers prototype and deploy efficient AI at the edge for smarter, low-latency, and scalable solutions.

Michael Connel