KI-Tutorials, Daten-Guides & Produkt-Updates
Praxisnahe Guides zu KI-Anwendung, Datenpipelines und dem Deployment eigener Modelle - kein ML-Expertenwissen nötig.
Internal tools have a bad reputation: they take months to build, look like they were built in 2008, and are immediately abandoned when the team that built them leaves. AI changes this. Here is how to build internal AI tools in days, not months - without writing a backend.
FinnA model that was accurate at deployment becomes less accurate over time as the world changes. Automated retraining pipelines detect drift, trigger retraining, validate the new model, and promote it to production - without manual intervention.
JuliaNaive 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.
Usama
Finance, healthcare, insurance, and pharma operate under compliance requirements that most AI workflow tools were not designed for. Here is what AI workflow automation actually requires in regulated environments - and how to build it.
FinnExplainability is no longer a research topic - it is a regulatory requirement. The EU AI Act, GDPR Article 22, and sector-specific regulations in finance and healthcare all require that AI decisions be explainable. Here is what that means in practice.
Julia
Data science teams build models. DevOps teams deploy them. The handoff takes weeks - and often kills the project before it reaches production. Here is how to deploy AI models as secure, production-ready APIs without a dedicated infrastructure team.
Anushka
Forecasting, classification, and generative AI are not three separate problems requiring three separate platforms. Here is what an all-in-one approach looks like and when it makes sense for enterprise teams.
UsamaNo-code AI pipelines remove the engineering bottleneck. Explainable AI removes the trust bottleneck. Together, they make enterprise AI deployable by the teams closest to the problem - without sacrificing accountability.
Julia