.1TechnologyAugust 18, 2026 Karina The Knowledge Layer Multi-Agent Systems Actually NeedEnterprises are building multi-agent systems backwards. All the investment goes into how agents communicate, almost none into what they reason on. Orchestration coordinates the workflow, but over ungoverned data it just helps agents pass flawed assumptions to each other faster. The fix is a shared knowledge layer
.2AI Across IndustriesAugust 4, 2026 Karina How Enterprise AI Should Actually Be Built: Context First, Model SecondMost enterprise AI starts with the model and recovers context later — which is why accuracy breaks in regulated work. The fix is to reverse the sequence: build a validated semantic layer before the LLM touches your data. See how a context-first architecture hit 97% accuracy where conventional RAG stalled at 80%.
.3TechnologyJune 25, 2026 Karina You're Running an LLM. But Do You Actually Know If It's Working?You deployed the model. The team tuned the prompt, the architecture, the pipeline. But do you know if the output is actually good? Most enterprises operate with an evaluation blind spot, catching failures only after they reach a customer. Here is what to measure, what to fix, and where to start today.
.4Use CasesJune 24, 2026 Karina How Yettel Hungary Is Using AI to Transform Customer Care - Without Losing Control of Its DataYettel Hungary's customer care teams were slowed by a fragmented knowledge base - hard to onboard into, slow to pull answers from. Denitsa Gavrilova, Director of AI and Data, explains why they chose a strategic AI partner over building in-house, and the lesson that reshaped their approach: the real work starts after launch.
.5TechnologyMay 5, 2026 Karina What Is the AI Context Layer, And Why It Changes Everything About Enterprise AIEnterprises are spending more on AI than ever, yet 95% of pilots deliver zero measurable ROI. The constraint is not weak foundational models. It is a fundamental lack of proper data context. The AI context layer sits between raw enterprise data and model output, delivering organizational knowledge at the exact point of inference.