Guides & expertise
Enterprise AI, no fluff
Real field experience, concrete workflows and the pitfalls to avoid, drawn from our engagements, to move from experimentation to AI in production: methods, architectures and governance. Expert content to help you decide and industrialise — not content churned out for the sake of it.
Our reference guides
From POC to production
The control points — security, tests, error recovery, observability, inference costs — that let a prototype actually hold up under production load.
n8n, MCP and governed LLM agents
Orchestrating reliable, secure LLM agents: the roles of n8n and MCP, idempotency, input/output guardrails and keeping token costs under control.
GenBI: generative BI without hallucination
Why an LLM wired straight to your database makes up figures, and how a governed semantic layer delivers answers you can trust and trace.
AI governance: GDPR, AI Act and trust layer
The minimum trust layer — logs, audit trails, evals, cost and access control — for AI that stays compliant and demonstrable to a regulator.
Mapping your data for AI
Process mining on real data and AI-readiness scoring: the data foundation without which any automation or assistant stays fragile.
An AI roadmap prioritised by ROI
Scoring use cases by value, feasibility and effort, weighing build vs. buy and LLM choices, and sequencing quick wins and structural projects.
Where to start?
If you are just getting started, begin with prioritising your use cases by ROI, then mapping your data: these are the two foundations. Next comes industrialisation (automation with n8n/MCP and agents), then governance (GDPR & AI Act). To turn these principles into an action plan tailored to your context, our assessment & scoping (short, fixed-price engagement, no strings attached) is the easiest way in. You can also browse all our services.
Latest articles
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The EU AI Act: a concrete action plan for enterprises in 2026
Obligations, timeline, risk classification: what you actually need to put in place, without panicking or over-investing.
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Enterprise RAG: why your AI assistants still hallucinate
An assistant that cites a document that doesn’t exist destroys trust in a single demo. The real causes of RAG hallucinations, and how to…
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AI project ROI: stop promising it, start measuring it
« 30% more productivity » means nothing. How to build a defensible ROI case, from scoping through to production monitoring.
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AI in production, not in POC: 5 reasons your POCs stall before scaling
Most AI POCs never become products. Here are the five most common causes — and how to defuse them at the scoping stage.
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Your data isn’t ready for AI: a 6-point diagnostic
Before training or plugging in an LLM, six checks decide whether your data will hold up or sink the project.
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GenBI and the semantic layer: the missing link between your data and natural language
Without a shared definition of your metrics, GenBI makes things up. The semantic layer is what separates a flashy demo from a reliable tool.
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N8N, MCP and LLM agents: governed automation, not a tangled mess
Orchestrating LLM agents with N8N and MCP without building unmanageable integration debt: architecture principles and concrete guardrails.
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GenBI: query your data in natural language, without hallucinations
Generative BI promises natural-language querying. The real risk: confidently wrong answers. How to make it safe.
Frequently asked questions
Is your content AI-generated?
No. Our content draws on real engagements: methods, architectures, pitfalls and lessons learnt in the field. We use AI to work fast, never to publish generic content — exactly what Google advises against and what our clients expect us to avoid.
Who are these resources for?
For leaders and CIOs steering an AI trajectory, and for the data and IT teams who implement it. Each theme speaks to both levels: the decision (ROI, governance) and the implementation (architecture, workflows).
How do these resources help me in practice?
They give you a framework to move from experimentation to production: which use cases to prioritise, how to industrialise and govern, and how to avoid the classic traps. To apply them to your context, our diagnostic turns these principles into a prioritised roadmap.
Can I suggest a topic or ask a question?
Yes. Write to us at contact@ianaos.ai: recurring questions feed our next guides and FAQs.
Move from experimentation to AI in production
Start with a short, fixed-price diagnostic: maturity, high-ROI use cases, and a prioritised roadmap. No commitment.