How developers actually build reliable AI systems amidst the AI boom
AI in production is harder than anyone warned you
The prototype ran like a dream. Then you shipped it, and now you’re dealing with failures that are hard to trace, timeouts, and workflows that lose state halfway through.
This ebook pulls together three detailed engineering case studies on what teams actually tried, what broke, and what finally worked — with real architectures and concrete tradeoffs, not theory.
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Three teams, three distinct challenges
A go-to-market platform that scores companies, enriches leads, and drafts outreach, all orchestrated across various third-party APIs — each with its own limits and failure modes.
Web scraping at serious scale: 600M+ records daily across 10,000+ sources. The pain was real: millions of jobs, constant failures, and the realization that traditional job queues just weren’t going to cut it.
An “AI teammates” company running 10M+ Temporal Activities per day on Temporal Cloud. An early lesson: without reliable orchestration, an agent is as good as a chatbot.
All our orchestration relies on Temporal today.
Aurelien Aubert
CEO, Cargo
Our engineers no longer have to worry about orchestrating retries or tracking job state. They focus on what matters.
Subrat Basnet
Co-Founder, Grepsr
It’s Temporal all the way down for orchestration. Scaling just means running more Workers.
Stan Polu
Co-Founder & CTO, Dust
The hard part isn’t the AI. It’s everything around it
AI demos are easy. Production is a different game: coordinating services, handling partial failures, managing retries, keeping track of workflow state.
Observability tells you something broke, but not why.
These stories cover what teams actually built and why: the architecture choices, the operational realities, and what matters once you're past the prototype.
Anyway, the ebook is free. 16 pages, real architectures, no fluff.