Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. These intelligent machines can be trained to perform various tasks by processing large amounts of data and recognizing patterns in the data. AI can be classified into two main categories: narrow or general. Narrow AI is designed to perform a specific task, while general AI is designed to perform any intellectual task that a human can. Some examples of AI include voice assistants such as Siri and Alexa, self-driving cars, and facial recognition software.
Your team processes hundreds—maybe thousands—of emails per day. Each one needs to be read, understood, categorized, and routed to the right person.
That’s a lot of human brainpower spent on triage.
What if AI could handle that categorization in seconds? What if urgent emails automatically escalated, routine inquiries queued for batch processing, and spam silently filtered—all without human intervention?
Building a Production-Ready Agent with Amazon Bedrock AgentCore + Strands Agents ✅ Executive Summary Organizations need scalable, secure, and flexible automation to handle complex business logic — especially in regulated environments (e.g. compliance checking,...
Retrieval-Augmented Generation (RAG) for NewbiesClients come to us all the time with the following requests: “We are actively looking for developers to help us build our envisioned not-yet-named AI.” We’ll hop on a call where we’ll start to discuss the idiosyncrasies...
🤖 Building an AI Code Review Agent for GitHub The complete guide to creating a GitHub PR Review Assistant with Amazon Bedrock AgentCore 📋 Table of Contents Overview & Architecture Prerequisites Environment Setup Building the Agent Testing & Deployment GitHub...
AI in Software Development: Benefits, Tools & Trends (2025)Artificial Intelligence (AI) has revolutionized the entire landscape of software development. From instant code generation to predicting bugs before they appear, AI in software development has evolved from...
How to Make an AI System? A Complete GuideAI systems are transforming how agencies and people work. They can automate responsibilities, examine data, and enhance decision-making, making processes quicker and more efficient. AI is shaping the future, from virtual...
Using Amazon Q Developer and ChatGPT to build solutions on AWS Last week, a prospective client came to us asking if the following was possible on AWS. To use an API to transcribe an audio file and apply audio intelligence. 1. Maintain high security and compliance. 2....
Clients frequently approach us with AI projects. This typically leads to discussions about the idiosyncrasies of Large Language Models (LLM), prompt engineering, parameter-efficient fine-tuning, and reinforcement learning from human feedback. Usually, the conversation leads to Retrieval-Augmented Generation.
In recent years, large language models (LLMs) have seen significant innovations and improvements in how they function and produce content. However, with this technology, there still could be some tweaks in how models can produce more authoritative responses with cited sources to boot. Retrieval-Augmented Generation was introduced to help the accuracy and access models have to external sources.
This blog post delves into the advanced capabilities of generative models, showcasing how they optimize maintenance strategies in the dynamic landscape of Internet of Things (IoT) solutions. From enhancing anomaly detection to reducing unplanned downtime, discover how generative models revolutionize predictive maintenance, offering a glimpse into the future of asset management. Join us on this journey of innovation and efficiency as we unravel the potential of generative models in shaping the future of IoT-driven maintenance strategies.