Notes on building AI that works in production
Practical writing from our engineers on agentic AI, RAG, MCP, AI security, compliance and the cloud infrastructure underneath it all.
How to Choose a Software Development Company: 12 Questions to Ask
12 questions to ask before hiring a software development company, plus red flags to watch for and how to compare local and offshore teams.
Read articleHow Much Does It Cost to Build an App in 2026?
Real app development cost ranges for 2026, what drives the price, hidden costs to budget for, timelines, and how to cut cost without cutting quality.
Read articleHow Much Does It Cost to Build an AI Agent?
What it really costs to build an AI agent: price ranges by complexity, the factors that drive cost, monthly running costs, timelines and build vs buy.
Read articleSave Token Cost: 7 Practical Ways to Cut Your LLM Bill in Production
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 articleAgentic Engineering: Picking the Right Agent for the Right Job
A 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 articleDoes AI Adoption Mean Deregulation? AI Regulation in 2026
AI adoption does not mean deregulation. Here is where AI regulation stands in the EU, Saudi Arabia and the UAE, and how to run AI compliance management in practice.
Read articleMVP Development: Cost, Timeline and What to Build First
A practical guide to MVP development: what an MVP should include, how much it costs, how long it takes and the mistakes that waste startup budgets.
Read articleRAG in Production: Retrieval Augmented Generation Done Properly
RAG that works for real users: chunking, hybrid search, reranking, permission-aware retrieval, freshness, evaluation, and when to skip RAG entirely.
Read articleSolution Architecture for AI Products: The Orchestration Layer
A reference solution architecture for AI products: gateway, orchestration layer, tools, memory, retrieval, guardrails and observability, plus what to build or buy.
Read articleClean and Secure Code Is Still Very Relevant in the AI Era
AI assistants make code cheap to write, not cheap to own. How review discipline, tests, scanning and traceability keep code manageable, secure and regulated.
Read articleMCP Explained: A Practical Guide to the Model Context Protocol
What 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 articleHow to Implement Guardrails for AI Apps (and Why Hack Proof Is a Myth)
How to implement guardrails for LLM apps: prompt injection defenses, tool permissions, schema validation, red teaming, and why no AI system is truly hack proof.
Read articleAccelerated Development on GCP with Terraform and AI Assistants
How to pair AI assistants with Terraform on Google Cloud to ship faster: module structure, environments, policy checks, Cloud Run, Vertex AI and guardrails.
Read articleAI Ops: How to Run AI Systems in Production Without Surprises
AI Ops for LLM apps: tracing, evals in CI, model version pinning, drift detection, cost and latency SLOs, and incident response for AI systems in production.
Read articleProtect Data Exposure in AI Systems with Data Classification
How to protect data exposure in AI systems: classify data before it reaches a model, redact PII, respect residency rules like Saudi PDPL, and lock down logs.
Read articleFrontline AI Agents: Putting AI in Front of Your Customers
What 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 articleHave a project in mind?
Tell us what you are trying to build. You will get an honest take on scope, timeline and cost, usually within one business day.