Category: Artificial Intelligence
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How to to build Agents and RAG by focusing on the problem
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Everyone is talking about AI agents and retrieval-augmented generation (RAG) like they are a new frontier reserved for a handful of AI specialists. They are not. A recent build inside GoTeams’ own presales platform, Partner Portal, shows why: an engineering team set out to fix a single, boring bottleneck, and ended up with something that…
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From Demo to Production: The 5 Zones of a Real AI Agent System
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Most AI agents pass their first test. A user asks a question, the model replies, and the demo looks finished. Then the agent goes live, usage climbs, and the answers get worse, not better. The model has not changed. The architecture around it was never built to hold up at volume. The gap is architectural,…
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What to Store Next to Your Embeddings: A Production RAG Checklist
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Every RAG tutorial ends at the same two steps: embed and query. You chunk your documents, store (id, embedding), run a nearest-neighbour search, and the demo works. Then you put it in front of real users and it quietly falls apart. The vector was never the hard part. What you store next to it is.…
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How helloparts automated PDF order intake with Claude
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With Claude, helloparts: At GoTeams, the AI delivery practice of GoGroup GmbH, we build production AI features for our partners. helloparts, the intelligent purchasing platform operated by GoParts Technology GmbH, lets automotive parts professionals order across brands from their preferred suppliers. They came to GoTeams with a problem hiding in plain sight: a share of…
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MCP Code Execution vs. Direct Tool Calling: How AI Agents Should Actually Use MCP
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TL;DR: Letting AI agents write and execute code instead of making direct MCP tool calls reduces token usage by up to 77% with no drop in task success rate. Anthropic, Cloudflare, and the open-source community are all converging on this pattern independently. Why AI agents should write code instead of calling tools – and how…
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The Comfort Zone Cycle: Why Corporates (and Countries) Keep Missing the Bet
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55% of European companies have integrated AI into at least two business processes — in the US, it’s 81% (EIB Investment Survey, 2025). German employees spend 1.7% of their working hours actively using AI. American employees spend 5.2%, more than three times as much (IZA@LISER Network, 2026). German companies put 29% of their investment into…
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Your AI Roadmap Is All Defense, No Offense
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The average company is running 21 AI projects at once, according to a Rackspace Technology survey from mid-2025. MIT’s Project NANDA found, in the same year, that 95% of generative AI pilots produce zero measurable P&L impact. Put those two numbers together and the conclusion isn’t that AI doesn’t work — it’s that almost all…
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Is AI a New Business Model? Or Just a New Layer on Old Ones?
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Germany founded 3,053 new startups in the first half of 2026, more than in all of 2024 and 52% more than the second half of 2025, according to the Startup-Verband and startupdetector’s Next Generation Report. A third of them, 1,038 companies, describe themselves as AI businesses. Read one way, that number is proof Europe is…
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From Claude to Browser, Live: How to Stream AI Estimates in Real Time
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There is a very specific kind of frustration that comes from staring at a loading spinner. You have submitted something. The page is doing something. You are not sure what. You do not know if it is 10% done or 90% done. You do not know if it crashed. You are just sitting there, watching…
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Driving Claude Code in Production: A Practical Playbook
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Most engineers pick up Claude Code and use it like a faster autocomplete. Bugs get fixed. PRs get out. It works fine. The actual leverage does not show up until you change how you are using it, and that change is mostly about habits, not prompts. The teams getting outsized results are not running a…