Your Dutch Elastic Partner

Consultancy & architecture

We design your Elastic architecture for observability, SIEM, or search, including sizing, retention, and the choice between Elastic Cloud, Serverless, or self-managed.

Implementation & migration

We'll set up Elastic and migrate your existing log sources, dashboards, and search indexes, with a parallel testing phase before you make the switch.

Licensing Advice

We'll help you choose between the Elastic Standard, Gold, Platinum, and Enterprise plans based on the features you need.

Managed Services

We manage your Elastic environment based on SLAs, from upgrades and index lifecycle management to monitoring your clusters.

Observability with OpenTelemetry and Elastic

Common Observability Challenges

Kubernetes clusters and microservices generate logs, metrics, and traces that end up scattered across separate tools, each with its own agent and license. The volume of logs often grows faster than the monitoring budget.

Consolidate your telemetry in Elastic Observability

We instrument your applications using Elastic Distributions of OpenTelemetry (EDOT), so your instrumentation runs on the open OTel standard. We run Elastic as a self-managed solution in your own environment or send your data to Elastic Cloud. With Streams, raw logs are automatically parsed and enriched, and Significant Events point you toward the root cause.

Advantages

No vendor lock-in on instrumentation, because OpenTelemetry is vendor-neutral. Lower storage costs per log thanks to logsdb, and SLOs and alerts that align with your Managed Kubernetes agreements with SUE.

RAG and AI search using Elasticsearch as a vector database

Common AI Search Challenges

Many AI pilots fail at the retrieval stage because the language model cannot find the correct internal documents or receives outdated context. A separate vector database alongside your existing search index is yet another system that needs to be secured and managed.

Build RAG on Elasticsearch

We combine keyword search and vector search (hybrid search) in Elasticsearch, using embeddings and rerankers via the Elastic Inference Service, including the multilingual models from the acquired Jina AI. With Agent Builder—which has been generally available since January 2026 with support for MCP and A2A—we connect agents to your own data using the language model of your choice. We start with an AI use case workshop led by our Data & AI team, so you first understand which question to answer with which data.

Advantages

Search, vectors, and agents on the platform you may already be using for logs or security. Requires less memory for large vector sets thanks to DiskBBQ, which searches vectors directly from disk.