Founder & CEO, Wavenetic
Denis founded Wavenetic to build AI products that actually run inside enterprise perimeters — banks, TSOs, regulated industrial operators, defence. Engineer first, CEO second. Writes about on-premise AI, multi-agent orchestration, OCPP systems, and what the EU regulatory frame actually means for AI procurement.
Sovereignty isn't a deployment choice — it's a nine-layer audit. Here's the buyer's guide that replaces the SaaS-vs-on-prem binary with a real decision rule.
An enterprise AI software factory is not a platform you buy or a velocity metric you chase. It is a governance-first operating model measured in auditable control points per merged change.
Closed-loop AI succeeds or fails on governance, action-layer wiring, and rollback — not model accuracy. Here is the six-stage architecture and the maturity ladder.
Slovenia's first public AI CCO for accounting, tax, and compliance is live in WaveFlow as a free public demo — with private cloud, on-premise, and air-gapped deployments available for regulated entities.
Stop framing AI agents as a cloud-or-local procurement choice. Build a policy-based routing layer that decides per-task where reasoning, memory, tools, and data execute.
Most enterprises asking for air-gapped AI need one of four distinct architectures. Picking the wrong one means paying air-gap prices for cloud-grade risk.
Enterprise AI agents fail in production because teams build them as standalone apps instead of governed digital workers on a shared control plane. Here's the sequencing that actually ships.
A line-item TCO model for on-premise AI: CapEx, OpEx, facility readiness, refresh cycles, and the utilization math that actually drives cost per token.
The cloud-vs-on-premise AI debate is the wrong frame. Enterprises win by classifying workloads—training, RAG, real-time inference, regulated data—and routing each to the right environment.
A reference architecture for private RAG built around security boundaries: ingestion zones, vector stores, policy engines, inference, and audit planes.
A practical framework for classifying enterprise AI workloads by sensitivity, latency, and compliance—then deciding what runs on-prem, hybrid, or in the cloud.
In 2026, financial AI isn't about the biggest cloud model — it's about deploying efficient, precise, and secure models directly on your proprietary data. Here's how Gemma 4 and WaveFlow make it real.