Katya Savenkova

Why Enterprise Wikis, Intranets, and SharePoint Fail to Preserve Institutional Knowledge

Most enterprises already own a wiki or SharePoint — and employees still walk to a colleague's desk. Why documentation theater happens, what distinguishes a true AI-native knowledge layer, and why connecting existing systems beats replacing them.

AI Audit Logs as Compliance Evidence: What to Capture, Retain, and Present to Regulators

Most AI platforms log conversations. Regulators need something different: a record of every governance control action the platform took. EU AI Act Article 12 mandates a minimum 6-month retention period for high-risk AI system logs. Here is what that log must contain and how to use it when inspectors ask questions.

How to Choose an Enterprise AI Platform: 8 Questions Every Compliance and IT Leader Must Ask

Enterprise AI vendor evaluations are dominated by model benchmarks and UI quality. The questions that actually determine whether a platform is deployable in a regulated organisation concern governance architecture, security depth, compliance tooling, and audit capability — criteria most platforms fail before the demo ends.

Enterprise AI Cost Control: Token Budgets, Per-Team Limits, and Real-Time Budget Alerts

Giving 250 employees unrestricted access to frontier AI models without cost controls is how you generate a $40,000 monthly API bill in week three. Here is how enterprise AI cost governance actually works — and why model choice alone creates a 25× cost variance per query.

Engram: How Persistent AI Memory Turns Every Interaction Into Organisational Intelligence

Enterprise AI is stateless by design — each session starts from zero regardless of how long the platform has been running. Engram fixes this with 9 memory types, 4 maturity stages, and self-organising gravity wells that accumulate institutional knowledge permanently.

How RAG Works in Enterprise AI — And Why Your Knowledge Base Architecture Determines Answer Quality

Enterprise AI vendors describe their knowledge base feature as "your AI trained on your documents." It is not. The accuracy of every answer depends on five architectural decisions about chunking, embedding, retrieval, and generation — most of them invisible to users.

Staff Augmentation Evolved: Three Strategic Models to Navigate the AI Era and Market Uncertainty