Plain-English answers to the questions founders and engineering leaders ask us most: which model to run, how to get documents agent-ready, and how to test, ship and run agents that stay accurate, secure and affordable.
Pay per token, use Bedrock, rent GPUs on EC2 or buy your own servers? A plain-English guide to the cost, control and effort of each LLM hosting option.
Long documents full of tables and “see section 4.2” links break simple AI tools. Here is a 7-step strategy that stays accurate and traceable at scale.
Why most company chatbots give confident wrong answers, and how hybrid RAG plus a simple reasoning loop fixes it. A plain-English guide with diagrams.
Claude Code bills grow with context, not keystrokes. 12 habits and 5 team controls that cut token spend without slowing your developers down.
AI agents give different answers each run and take real actions. Here is a practical QA framework, release process and toolset to test them well.
AI agents change when prompts, models or data change, not just code. Learn how to version, test, release and monitor them safely in production.
An SHVX assessment gives you a written review of your data, use case or agent, and a recommended next step.
