When your codebase grows faster than your team's ability to understand it, institutional knowledge becomes a bottleneck. Learn how CoreRAG uses real-time GitLab webhooks and Graph-RAG technology to turn dense repository data into plain-language answers, helping developers, sales teams, and tech leaders make smarter, faster decisions.

How an AI–Physics Hybrid Generalized to Unseen Antibodies—Without Benchmark-Specific Training Cross-validation can show how well a developability model performs on familiar data. But in real drug discovery, the molecules being evaluated are usually new. The real challenge is whether a model can remain reliable when it encounters molecules it has never seen before. We tested this by evaluating Apotek Landscape zero-shot on GDPa3, the blinded test panel from the 2025 Ginkgo AbD

You've probably heard of RAG (Retrieval-Augmented Generation), the technique that lets AI systems look things up before answering, rather than relying purely on memory. But here's something most people don't know: the way AI searches for information matters enormously. There isn't just one way to retrieve knowledge. In fact, there are several query modes, each with its own strengths: Dense, Sparse, Hybrid, and Graph RAG. Our product supports all of these, so let's break down

Synthetic Data Generation (SDG) is overturning the oldest assumption in AI training: that real-world data is always more credible. Field results say otherwise — better generalization, at half the cost.