.1TechnologyJune 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.
.2Use 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.
.3TechnologyMay 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.
.4AI Across IndustriesApril 21, 2026 Karina Agentic AI in the Enterprise: What It Is, What It Promises, and What It Needs to Work Agentic AI is the next leap beyond generative AI: systems that don't just answer questions, but plan, decide, and execute multi-step work autonomously. The promise is massive but most enterprises lack the data infrastructure, governance, and integration layer to make it work. Here's what agentic AI actually is, and what it takes to deploy it safely.
.5Neuralith™December 9, 2025 Ada How Enterprises Can Leverage AI for Competitive Advantage: Retrieval-Augmented Generation (RAG)With innovation happening at lightning speed, enterprises need real-time, contextual insights — not last month’s static reports. But let’s face it: traditional competitive analysis is too slow, too manual, and too surface-level to keep pace with modern demands.