Web Technologies Articles
37 posts found
The GenAI Divide: Why 95% of AI Projects Fail And How to Be The 5%
Despite billions invested in Generative AI, 95% of enterprise projects fail to deliver business value. Learn why most pilots stall, what the successful 5% do differently, and how cloud-native data engineering, real-time processing, and agentic AI can help you cross the GenAI Divide.
Why do most GenAI projects fail? Discover the key reasons and the path enterprises can take to achieve real ROI with AI.
RAG Architecture & Vector Databases: What AI Agents Need to Succeed
Building AI agents that reason with real-time, accurate data starts with a RAG architecture. Learn how to select vector databases, optimize context retrieval, and scale pipelines for production.
Discover how RAG architecture and vector databases make your AI agents more accurate, scalable, and context-aware.
Real-Time Data Is the New Default: Why Batch Processing Holds AI Back
Stale data is killing your AI and ML models. This article shares differences between using batch vs real-time data processing for AI, industry examples, and the dangers of decision latency.
85% of AI models fail before launch often due to bad data. Discover how using real-time data for AI unlocks smarter decisions, faster response, and stronger ROI.
Building an AI Agent: The Right Stack for Product Teams
In just a few years, we’ve gone from prompting static models to building autonomous agents. This guide breaks down the components of an AI agent stack, designed for product teams that want to move from idea to execution.
Confused by AI agent architecture? This guide breaks down the modern AI agent stack: models, frameworks, and orchestration explained for product-led teams.
AI-Ready Data Infrastructure: Can Your System Support AI?
Your data infrastructure can make or break your AI initiatives. In this article, we’ll explore different AI implementation challenges and teach you how to assess your current data architecture to make it AI-ready.
85% of AI models fail due to data. Here’s how you can assess your current infrastructure’s AI-readiness and plan to improve it before launching smart features.
Real-Time Data & Cloud Analytics: Unlock EV Charging Revenue
Real‑time data turns EV chargers from simple power outlets into strategic, revenue‑generating assets. Here’s how Charging Point Operators can use cloud analytics to unlock new income streams, optimize locations, and delight drivers.
Learn how real-time data and cloud analytics help CPOs unlock new EV charging revenue, optimize locations, and improve driver experience.
6 Ways Automation Boosts Cost Efficiency in EV Charging
Leverage dynamic load balancing, remote monitoring, predictive maintenance, and self-service customer support to improve your EV station performance while reducing operational costs. Here’s how.
Cut EV charging station costs with automation for load balancing, customer support, predictive maintenance, and more.
EV Charging Station Requirements: Launch Your Operation
Setting up an EV charging business is about more than hardware and permits. It needs scalable tech foundations that support real-time data and ensure network reliability. Learn everything you need to know before launching an EV charging station.
Discover the US key EV charging station requirements regarding physical infrastructure, regulations, technology, and funding.
4 Ways CPOs Use Real-Time EV Data to Maximize Uptime
As a charge point operator (CPO), you’re the middleman between the physical EV charging stations and the back-end. Having access to real-time data can help you improve performance and revenue. Here’s how.
Use real-time EV charging data to maximize uptime, optimize pricing, improve the customer experience, and make better decisions. Here’s how.
Reducing Fleet Downtime with Real-Time Data Analytics
Every minute an EV fleet is out of service means lost revenue, inefficiencies, and customer dissatisfaction. Discover how real-time analytics can transform your operations to minimize downtime and optimize costs.
Downtime isn’t just an operational challenge—it’s a strategic risk that affects revenue, asset utilization, and scalability. In an uptime-critical industry, reactive maintenance isn’t enough; real-time insights are the key.
In-House vs. Outsourced Data Engineering: Which Is Best?
Data collection, processing, and analysis are essential for gaining a competitive edge and driving innovation. But should you build this capability in-house or outsource to data engineering experts? Let’s break down the pros and cons.
Building, managing, and scaling efficient data pipelines requires a skilled team. The question is—should you develop that expertise in-house or leverage an external team to drive smarter, data-driven decisions?
AI in Customer Experience: Boost Engagement and Cut Costs
Wow your customers at every stage of the lifecycle with AI-powered features. Learn the benefits of using AI in customer experience, real-life examples, and pitfalls.
AI models allow you to automate repetitive tasks, drive insights from data, and improve the customer experience. Here’s how.