17 / Guides / Engineering
Engineering AI & Automation That Ships
The build practices behind automation that survives contact with production.
A working demo and a system that runs every morning, unattended, are different engineering problems. The second needs dedup, retries, monitoring, and a way to fail loudly instead of silently.
We build automations as real software — tested, documented, and handed over with the source — so they can be maintained and extended rather than rebuilt.
This cluster is the technical side: how we structure builds, handle edge cases, and leave behind systems a team can own.
In this guide
- Automating sensitive data without creating a compliance problemJune 6, 2026
- Google Apps Script: the most underrated automation toolMay 27, 2026
- The security review every AI integration needs before launchJanuary 22, 2026
- Monitoring AI systems like you monitor infrastructureAugust 15, 2025
- CRM integrations break in the same five placesJuly 28, 2025
- Evals are the part nobody wants to buildApril 23, 2025
- What retrieval-augmented generation actually gets wrongJanuary 14, 2025
- Monitoring automation you can't watch manuallyNovember 12, 2024
- Idempotency: the boring fix that prevents the worst automation bugsOctober 21, 2024
- Cost control for AI pipelines before the bill surprises youJune 18, 2024
- Before you build a CRM integration, answer these five questionsApril 23, 2024
- The real cost of a broken integrationJanuary 11, 2024
- 128k context windows: what actually changes when the model can read moreNovember 14, 2023
- Prompt injection is the SQL injection of this generation, and most teams aren't readyOctober 11, 2023
- What we learned putting frontier models in production this yearJuly 14, 2023
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