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12 articles on LLMs from our senior engineering team: practical lessons from building, scaling and rescuing high-stakes platforms.
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 senior-led guide to vetting and hiring generative AI engineers — what to test for, the red flags, and why career engineers beat resume keywords.
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 our senior engineers to identify quick wins and show you what's possible.