Blog
Articles on building and running AI in production.
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Jev in production: putting TypeSafe's System One model behind real traffic
How TypeSafe's Jev works, where it fits next to LLMs, and the timeouts, fallbacks, evals and confidence thresholds to put around it before it touches real traffic.
- AI engineering
- TypeSafe Jev
- LLM architecture
- Production
2 min read
When to move an automation off no-code
No-code is the right way to find out whether a workflow is worth having. These are the signs it has outgrown the tool, and how to move it without a big rewrite.
- Automation
- No-code
- Production
2 min read
Agents or workflows: start with the workflow
Most systems described as AI agents work better as workflows with AI steps. How to tell which one you need, and what an agent needs before it acts in production.
- AI agents
- Architecture
- Production
2 min read
Before you build RAG: four checks
Retrieval-augmented generation solves one specific problem. Four checks that tell you whether you have that problem before you build the pipeline.
- RAG
- LLM architecture
- Evaluation
2 min read
Why AI projects fail after the demo
AI projects rarely fail at the demo. They fail months later in production, for reasons that were visible on day one: no evaluations, no owner, and releases that can't be undone.
- Production
- Evaluation
- Deployment
