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11 LLMs articles on production outcomes, with lessons from real enterprise projects. Read the guides that fit your next decision.
Autonomous agents were the 2025 story. In 2026 enterprise teams are re-architecting them into deterministic workflows with LLMs as components. Here is the…
How we took a Fortune 500 retailer's LLM inference spend from $4.1M to $1.6M annualised in 90 days without downgrading the model or degrading customer…
The default Kubernetes autoscaler was built for stateless web apps. Here's the GPU-aware inference autoscaling stack we deploy for enterprise LLM workloads.
Every enterprise AI programme reaches a point where direct model calls stop scaling. Here's the LLM gateway pattern we deploy, and why.
How we took a global support copilot from 4.8s P95 to 780ms in eight weeks — the bottlenecks, the architectural calls and what the team got wrong first.
A practitioner's playbook for controlling GPU and AI infrastructure costs at scale — the wasteful patterns we see most and the levers that move spend.
How we red team production LLM and agent systems for enterprise clients — the attack taxonomy that matters, our six-phase process, and where to invest first.
A practitioner's guide to designing offline and online LLM evals for enterprise systems — golden datasets, LLM-as-judge, CI gates, and what to alert on.
500+ enterprise projects give us a clear view: here's what's working in enterprise AI in 2026, what isn't, and where the real ROI is landing.
Why most RAG systems fail in production — and how to fix them. Chunking strategies, retrieval tuning, and hard lessons from real enterprise deployments.
When to fine-tune an LLM vs use RAG. Practical decision framework, hidden costs, and 5 production patterns from senior engineers.
Book a free 30-minute discovery session with us to identify quick wins and show you what's possible.