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AI Proof of Concept: What to Prove, What It Costs, and Who to Hire
What an AI proof of concept should prove, how it differs from a prototype or pilot, what drives cost, and how to choose a PoC partner.
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AI engineering insights
NVIDIA OpenShell on AWS: Your Agent, Your Rules
See how the model, agent, and OpenShell fit together—and watch a real task inside Software Sushi’s AMI, now on AWS Marketplace.
Can We Send PHI to an LLM?
A practical framework for evaluating LLM vendors, BAAs, data flows, retention, access, and AI behavior before a healthcare team sends PHI.
How to Evaluate RAG Retrieval Quality Before You Tune the Model
A practical framework for evaluating RAG retrieval separately from generation with labeled queries, layered metrics, human review, and release gates.
When Is Your Application Ready to Handle PHI?
A practical framework for assessing whether a healthcare AI application is ready to handle PHI across data flows, vendors, controls, testing, and operations.
RAG vs. Fine-Tuning: How to Actually Choose
How RAG and fine-tuning differ, what enterprises actually use, and how to choose based on changing data, evaluation, and total cost.
Why Machine Learning Models Quietly Get Worse After Launch
Why production ML models lose accuracy over time, how aging differs from drift, and what effective monitoring and intervention look like.
Why Most AI Pilots Never Reach Production (And What Separates the Ones That Do)
Why promising AI pilots stall before production, and what successful teams do differently across data, workflows, ownership, and integration.
How to Plan Your First AI/Tech Project (Without Wasting Time or Money)
A practical guide to planning AI projects that succeed — covering common pitfalls, MVP strategy, and the strategic groundwork most teams skip.
AI Forecasting Mistakes: The $10K AI Project That Almost Cost $40K
This post breaks down a common but costly mistake in AI forecasting: data leakage from random splitting — and shows how the right validation approach turned unreliable predictions into trusted business decisions.
Designing a Browser Extension using AI and Machine Learning; A Guide
A blog covering the basics of developing a chrome extension, as well as how to integrate one with the OpenAI API.
Object Detection using YOLO
Building on the OpenCV series, we turn to YOLO to detect any custom object class you want — walking through training the model and running detection end to end.
Building a Powerful Content Moderation Classifier with Scikit-Learn and LightGBM
Content moderation is a critical task for any online platform. The goal is to automatically identify and flag content that violates community guidelines, covering categories like hate speech, harassment, or self-harm.
Building a Recipe Assistant with AI
Imagine having a virtual assistant that understands user preferences, context, and queries with precision—providing relevant and tailored responses in seconds. This is the promise of an AI-powered chatbot, a tool designed to make interactions more intuitive, efficient, and personalized using OpenAI API and Retrieval-Augmented Generation (RAG) frameworks such as LangChain.
Face Detection with OpenCV
In this first blog of a new three part series in OpenCV, we will be covering face detection using OpenCV.
OpenCV Edge, Shape, and Feature Detection
A hands-on OpenCV guide to Sobel, Laplacian, and Canny edge detection, contours, morphological operations, template matching, Fourier and Hough transforms, and histogram equalization.
OpenCV Filtering, Thresholding, and Image Segmentation
A practical OpenCV guide to color space conversion, blurring and filtering, thresholding, Otsu binarization, GrabCut, connected components, and watershed segmentation, with Python code.
OpenCV Image Manipulation: Transforms, Arithmetic, and Masks
A hands-on OpenCV guide to geometric transforms, image arithmetic, bitwise operations, masking, and color channels, with Python code and output for every technique.