INSIGHTS
Practical notes for deciding what to build, what to audit, and what to avoid before an AI project reaches production.
AI consulting in Madrid: RAG, agents, and CTO help
A practical guide for Madrid and European teams hiring AI consulting for RAG, agents, LLM integration, architecture review, or fractional CTO support.
AI technical audit for RAG and agent systems
What to review before scaling a RAG pipeline, AI agent workflow, or LLM product: retrieval quality, evals, traces, cost, privacy, and vendor risk.
Remote AI consulting for European teams: RAG, agents, and LLM products
How to hire remote AI consulting for production RAG, agent workflows, LLM integration, and technical audits without buying a generic transformation project.
Remote fractional AI CTO: when a startup needs senior technical leadership
When a remote fractional AI CTO helps with architecture, hiring, vendor choices, technical due diligence, and production risk before a full-time CTO hire.
AI agent workflows in production: architecture guide
How to design AI agent workflows with tools, state, traces, evaluation, limits, and human handoffs before putting them in production.
Fractional CTO for AI startups: when it makes sense
How founders decide between fractional CTO, full-time CTO, technical advisor, or senior engineer when building AI or software products.
RAG vs fine-tuning for enterprise AI systems
How to choose between RAG, fine-tuning, both, or neither for internal knowledge, support, SaaS, and enterprise LLM systems.
Multi-agent systems for business: when they work
When multi-agent systems help a business, when a single agent is better, and what to review before putting AI agents into production.