Pimcore vs Prometheus

UK Investor Tenzing Acquires Majority Stake in Austrian MarTech Pimcore

UK-based technology investor Tenzing has acquired a majority stake in Salzburg-based data management software provider Pimcore. Pimcore, founded in 2009, operates an open-core platform that unifies product, customer, and digital asset data into a single source of truth, serving over 400 enterprise clients globally. The company was previously backed by Munich-based Nordwind Growth, which led a $12 million Series B in 2022 and held approximately a 73% stake. Pimcore's founders, Dietmar Rietsch and Matthias Blauth, will remain on board as co-CEOs to guide international expansion and target potential acquisitions, positioning clean data as a crucial baseline for enterprise AI applications. The acquisition is Tenzing's second deal in the DACH region and is currently pending antitrust regulatory clearance.

• UK tech investor Tenzing is acquiring a majority stake in Salzburg-based Pimcore.
• Previous majority shareholder Nordwind Growth led a $12 million Series B in Pimcore in 2022 and held a 73% stake.
• Pimcore transitioned from an open-source community model to an open-core approach, driving organic growth.

We built the layer enterprise AI is about to crash into. Now we scale it.

Today Nordwind Growth announced an agreement to sell its majority stake in Pimcore to Tenzing, a London-based investor in European B2B software. The transaction remains subject to antitrust approvals.

RAG Optimization Cuts Latency 40% with Bayesian Search

This six-month production case study describes scaling Retrieval-Augmented Generation by replacing naive fixed-token chunking with document-aware strategies (recursive clause/function splitting for contracts and API reference, semantic chunking for support tickets, and agentic LLM chunking for internal wiki), deploying a hybrid retrieval stack (BM25 + vector fused via Reciprocal Rank Fusion, then cross-encoder rerank top 50 → top 5), adding query transformation/expansion (3–5 generated queries), and automating Bayesian hyperparameter optimization with Optuna on a stratified ~200-query golden set. Observability (Prometheus, sampled golden-set evaluation, query telemetry) and A/B feature flags enabled continuous evaluation. Optuna produced a recall–latency Pareto frontier and selected a Balanced production configuration (recall@10 95%, p95 latency ≈320ms). Over six months recall@10 rose 78%→95%, p95 latency fell 850ms→320ms, hallucination dropped 12%→3%, and cost/query fell $0.008→$0.005.

• Six-month impact: recall@10 78% → 95% (+17 pp); p95 latency 850ms → 320ms (−62%); hallucination rate 12% → 3% (−75%); cost/query $0.008 → $0.005 (−38%).
• Document-aware chunking with per-type configs and example recall@10: contracts (recursive, chunk_size=1024, overlap=100) 94%; API reference (recursive, 768 tokens) 96%; support tickets (semantic, 512 tokens) 91%; internal wiki (agentic LLM chunking, 1500 tokens) 97%.
• Hybrid retrieval pipeline: BM25 + vector search fused via Reciprocal Rank Fusion, then cross-encoder rerank (top 50 → top 5); reranker adds ~50ms and yields ≈+15 percentage points recall in the rerank stage.

Practical Observability with OpenTelemetry and Prometheus

This technical guide explains how to implement production-grade observability for a Node.js microservice using OpenTelemetry, Prometheus, Grafana, and automated CI/CD validation with GitHub Actions. The article provides a complete, production-ready checkout endpoint example that instruments counters and histograms to capture throughput, status dimensions, and latency distributions with high-cardinality attributes. It advocates writing against the vendor-neutral OpenTelemetry API to avoid vendor lock-in, using the Prometheus exporter to expose metrics (port 9464), and visualizing percentiles (p95/p99) in Grafana. The repo layout includes Prometheus/Grafana docker-compose manifests, unit tests, and a GitHub Actions pipeline (checkout, Node setup, linting, tests) to validate telemetry and deployment. The post emphasizes multidimensional metrics over flat metric names and records best practices for structured logging and CI-driven telemetry validation.

• Article provides a production-ready Node.js microservice example instrumenting a checkout endpoint with OpenTelemetry.
• Uses OpenTelemetry PrometheusExporter to expose metrics (noted as running at http://localhost:9464/metrics).
• Defines a payment_requests_total counter and payment_processing_duration_ms histogram to capture throughput, status, and latency.