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AI Production Readiness Checklist for CTOs
What production-ready actually means An AI feature is ready for production when your team can measur...
Building a Multi-Agent AI System That Actually Works in Production
The demos for multi-agent AI frameworks look incredible. Autonomous agents planning, executing, and ...
Vector Databases Explained: Choosing the Right One for Your AI Application
Vector databases are now a core piece of production AI infrastructure. Whether you're building a RAG...
Designing AI-Powered APIs: Patterns and Pitfalls
Building an API that wraps an AI model sounds straightforward — take input, call model, return outpu...
Fine-Tuning Llama 3 with QLoRA: A Practical Guide
QLoRA (Quantized Low-Rank Adaptation) changed the economics of LLM fine-tuning. Before QLoRA, fine-t...
GPU Inference on a Budget: Optimizing Costs for Self-Hosted Models
Once your AI application reaches sufficient scale, the math on self-hosted model inference often sta...
LLM Inference Cost Optimization: A CTO Playbook
Where inference spend really comes from The most durable way to reduce LLM inference cost is to unde...
How CTOs Should Build an LLM Evaluation Framework
The evaluation decision CTOs need to make An LLM evaluation framework converts "does this feel bette...
The Complete Guide to LLM Evaluation in Production
Evaluating LLMs is one of the least glamorous parts of AI engineering — and one of the most importan...
The MLOps Foundation CTOs Need for Reliable AI Products
The operating model behind reliable AI MLOps is the set of operating capabilities that lets a team c...
From PoC to Production: An Honest Engineering Timeline
One of the most frequent conversations we have with new clients starts the same way: "We have a work...
Prompt Engineering at Scale: Moving Beyond Hacks
Prompt engineering has a reputation problem. For many developers, it conjures images of trial-and-er...
How to Make a RAG System Reliable in Production
The reliability boundary in RAG A reliable RAG system retrieves authorized, current, relevant eviden...
RAG vs Fine-Tuning: How to Choose the Right Approach for Your LLM
One of the most common questions we get from engineering teams is: "Should we fine-tune our model or...
Reducing LLM Inference Costs by 60%: A Case Study
When a B2B SaaS company came to us, their AI features were a success story — too successful. Their m...
Securing AI Applications: The Threat Model You Haven't Thought About
Enterprise teams investing in AI security are mostly focused on the wrong things. Compliance checkli...
Why Your LLM Prototype Fails in Production (And How to Fix It)
Every week, a startup team demos their new LLM-powered product and it looks brilliant. The model ans...