Practical writing from our engineers on agentic AI, RAG, MCP, AI security, compliance and the cloud infrastructure underneath it all.
How to save token cost on LLM apps: prompt caching, model routing, context trimming, batching, output limits, answer caching and measuring cost per task.
Read articleA practical guide to agentic engineering: when to use one agent or many, how to pick models per task, design tools, and place human checkpoints.
Read articleRAG that works for real users: chunking, hybrid search, reranking, permission-aware retrieval, freshness, evaluation, and when to skip RAG entirely.
Read articleA reference solution architecture for AI products: gateway, orchestration layer, tools, memory, retrieval, guardrails and observability, plus what to build or buy.
Read articleWhat MCP is, how it differs from function calling and APIs, how to build MCP servers for internal systems, and how to secure them with OAuth and audit logs.
Read articleWhat it takes to put AI on the frontline with customers: voice agents, WhatsApp and web assistants, Arabic and English, human handoff, SLAs and what to automate first.
Read articleTell us what you are trying to build. You will get an honest take on scope, timeline and cost, usually within one business day.