AI & ML
Field notes from our engineers on AI, product engineering, cloud, data and the craft of delivering software that lasts.
Articles
7 articles
AI Agents vs Traditional Automation: What Should Your Business Build?
How AI agents and rules-based automation really differ, why a chatbot is not an agent, and a practical step-by-step test for deciding which approach fits each part of…
RAG vs Fine-Tuning: Which AI Approach Is Right for Your Business?
A practical way to choose between retrieval-augmented generation and fine-tuning: separate knowledge gaps from behavior gaps, compare costs and risks, and see which approach fits common business scenarios.
How to Integrate AI Into an Existing Business Application
An engineering guide to adding AI to software you already run: choosing the first workflow, readiness checks, four integration patterns, legacy systems without APIs, security and a phased…
From AI Proof of Concept to Production: What Changes?
What really changes when an AI prototype becomes a production system: data pipelines, measured quality, reliability, governance and ownership, plus practical readiness gates for deciding when to launch.
OMANGOM Joins Anthropic’s Claude Partner Network
OMANGOM has joined Anthropic's Claude Partner Network, strengthening our Claude expertise through technical resources, partner enablement and ongoing learning.
How to Build a Production-Ready RAG System: A Practical Guide
Retrieval-augmented generation looks easy in a demo and hard in production. Here's the architecture, the trade-offs and the checklist we use to build RAG that customers actually rely…
Generative AI Use Cases for Business: 12 Ideas That Deliver Real ROI
Twelve practical generative AI use cases grouped by business function, with a simple way to prioritize them, the data and risk questions to answer first, and a path…