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21 insights
Latest An AEO audit for businesses in Ireland checks crawl access, business facts and assistant answers, leaving a dated baseline and a clear list of fixes.
Connect AI meeting notes to your CRM, correct the record with an LLM and prepare useful follow-ups, tasks and updates, with prompts and a skill template.
Why ChatGPT, Google AI Overviews and Copilot leave some small businesses out of their answers: 6 causes you can check and a 15-minute self-check.
Use ChatGPT to match enquiries, act on meeting notes, fix spreadsheets, answer support questions and turn a case study into content. Includes reusable prompts.
Compare AI tools for Irish small businesses, build a sensible monthly budget and weigh subscriptions and automation against the staff hours they could free up.
See how an Irish accountancy practice can track partial records, avoid stale reminders and test a document-chasing workflow with a copyable checklist.
Save your process as an AI skill, then run it with a URL or file. See a business analysis example, ChatGPT and Claude guidance, and free business templates.
Kitchens, a supplier and an EPOS vendor all see the same hospitality forecasting problem differently. That is exactly why nobody fixes it.
AI implementation for SMEs starts with one useful workflow, clear boundaries and human approval. A practical guide to choosing the right first project.
MCP, A2A, AP2, KYA, ATP, AGNTCY. The protocols showing up in every AI conversation, explained without hype or vendor framing.
Anthropic's financial-services repo is interesting because of the artefacts, deployment paths, data connectors, and human checkpoints around the agents.
Founders lose time to context scattered across tools and to the hours they can spend at a keyboard. Obsidian plus coding agents is starting to change both.
Many AI pilots work and still never reach production. The reasons are rarely technical: five patterns we see and three pilot-design questions that help.
EU AI Act compliance for agent systems is not a documentation task. The requirements that matter are built into the architecture or bolted on later.
Agent systems that work at launch can quietly degrade by month six. The decay modes, the operating discipline that prevents them, and who owns it.
An implementation reference for the routing, state, contracts, observability and recovery patterns that keep multi-agent orchestrators reliable.
In a multi-agent system the orchestration layer, not the agents, is the product. What it does, how to architect it and why the framework matters less.
Most teams should build operator fluency with existing AI tools before commissioning agent systems. Why that unglamorous first step saves money.
Most teams pick the wrong first automation. The ranking we use: leverage, frequency, tractability and reversibility, and why the loudest pain misleads.
Six disciplines that decide whether a multi-agent system survives production, and the two failure modes you only see once it is live.
Single agents work. Multi-agent systems work differently, and that difference is the whole game for operational functions.
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