KI-Tutorials, Daten-Guides & Produkt-Updates
Praxisnahe Guides zu KI-Anwendung, Datenpipelines und dem Deployment eigener Modelle - kein ML-Expertenwissen nötig.
Naive vector search gets you 60-70% of the way to a useful RAG system. The remaining 30-40% - the cross-document questions, the entity-specific lookups, the questions that require synthesising information from many sources - requires a knowledge graph.
AnushkaEnterprise AI projects stall on data unification. CRM records are in Salesforce. Contracts are in SharePoint. Customer emails are in Outlook. Making all of it available to AI - consistently, securely, and without an army of data engineers - is the real hard problem.
UsamaBuilding a RAG system on internal documents is straightforward in a demo. Making it secure enough for enterprise use - with proper access control, encrypted embeddings, audit logging, and role-based retrieval - is a different problem entirely.
Anushka
Most enterprise AI stacks are stitched together from five different tools. Each handoff point is a failure point. Here is what a unified AI platform that covers RAG, agents, dashboards, and API deployment actually delivers.
UsamaLearn how to connect your S3 bucket to aicuflow, index your files automatically, and start asking complex questions about your data - all without writing a single line of code.
Julia
RAG evaluation is one of the hardest problems in production AI. You can measure faithfulness, relevance, and recall - but knowing which metrics actually predict production quality requires a deeper look.
Julia