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AI & AgentsThe Bottleneck Was Never Intelligence. It Was Context.
Rakuten reported a seven-hour Claude Code session on vLLM with 99.9% numerical accuracy for one method. What the vendor case study shows, what it does not, and how to build a reviewable agent workflow.
AI & AgentsChatGPT Enterprise vs Copilot vs Custom RAG
Which should a DACH company shortlist? A neutral procurement guide to workflow fit, product terms, location controls, permissions, compliance roles, and total operating cost.
Business & RegulationEU Data Residency for AI APIs in 2026: Provider Guide
Compare EU storage and inference guarantees across OpenAI, Azure, Mistral, AWS and self-hosting. Current endpoints, transfer caveats and a procurement checklist.
Delivery & QALovable, Bolt, and Replit App Due Diligence
Before you raise or sell on a vibe-coded app, three questions decide whether it survives diligence: is it safe, do you own it, can anyone maintain it? The security, IP, and investor checklist.
Business & RegulationAI Funding in Austria 2026: aws, FFG, Premium, KMU.DIGITAL
The four AI funding routes in Austria for 2026, honestly assessed: aws for adoption, FFG for R&D, the 14% research premium, and KMU.DIGITAL. Plus the catch nobody mentions: open calls and budget.
Leadership & TeamsHow to Roll Out AI Internally in 2026
A staged internal AI rollout: align the team, map the process and alternatives, define a baseline, settle data and risk questions, pilot one bounded workflow, then expand or stop on evidence.
AI & AgentsMCP vs RAG vs Agent Skills vs Custom GPTs
Not four answers to one question, but four layers: RAG for knowledge, MCP for connectivity, Skills for procedure, Custom GPTs as a packaged surface. A decision tree, and why you usually compose them.
Leadership & TeamsFractional CTPO vs CTO and CPO
When one combined product-and-technology lead can reduce handoffs, when to split the remit, and how to assess capacity, independence, decision rights, and continuity without a fixed team-size rule.
Product & MVPTechnical Due Diligence for AI MVPs Before Funding
A practical evidence-room checklist: evaluation design, serving configuration, task-level economics, privacy, security, data rights, and the artifacts a reviewer can test.
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