There has never been more content about AI.
Tutorials, threads, frameworks, and hot takes are being published at a pace no one can realistically keep up with. For anyone trying to learn, build, or make grounded technical decisions, that creates a clear problem: separating signal from noise and finding what actually holds up in production.
This blog is about the signal.
What you'll find here comes directly from real enterprise work: building and running data and AI systems inside complex organizations. Places where production means dealing with legacy architecture, imperfect data, strict security requirements, governance, scale, hard deadlines, and real users.
The authors are the people doing that work every day: forward deployed engineers, AI engineers, data scientists, architects, and other technical specialists from Indicium AI's 600+ experts across five global locations, delivering for more than 50 enterprise clients.
They'll share the details a case study doesn't have room for: the technical decisions behind complex architectures, which LLM evaluation approaches held up in production and which didn't, the trade-offs made when the ideal solution met real-world constraints, and honest breakdowns of what broke and what they learned from it.
What to expect
- 💻 Code
- 📊 Benchmark results
- 🏗️ Architecture decisions
- ⚖️ Trade-off analysis
- 💬 Opinions, backed by real work
- 🔥 Lessons from what worked and what didn't
Use what's useful. Question what doesn't fit your context. Build better systems with the rest.
Welcome. 👋
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