Inside Meltwater Engineering.

The Meltwater Paris AI team attended the inaugural PyTorch Conference Europe at Station F. Here are our key takeaways on inference maturity, GPU kernel tooling, hardware co-evolution, and what it means for our AI platform.

How we reduced inference costs by 45.5% while improving accuracy by 3.02% by rethinking how our entity-level sentiment system processes documents.

How we tracked down and fixed a memory leak in a Kubernetes-hosted Node.js service after upgrading from Node 18 to 22, uncovering issues with closure retention, caching logic, and V8 engine changes along the way.

How we built a simple yet powerful event-driven system using MongoDB change streams and AWS SNS/SQS to replace polling-based architectures and deliver real-time user data changes to downstream consumers.

How we used pgcopydb to migrate a 1TB PostgreSQL database from AWS to Azure, covering migration strategies, Kubernetes execution, and the pitfalls we encountered along the way.

From merging pull requests to growing teams — how a 50/50 Team Lead experiment at Meltwater became a full identity shift, one uncomfortable conversation at a time.

We built URLBank, a data-driven system that automatically discovers the best entry points for web crawling using temporal link signals. The research has been accepted at The ACM Web Conference (WWW) 2026. Here's how we rethought seed selection from the ground up.

Meltwater's French AI team hosted the Paris NLP meetup, sharing how we built MIRA, a scalable RAG stack processing billions of documents per day.

How we integrated the Maven Build Cache Extension to speed up JVM builds by reusing outputs from previous builds, both locally and in CI/CD pipelines.