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Evals for Qdrant

8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Qdrant AI products.

AI Platform
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About Qdrant

Qdrant is an open-source vector database and similarity-search engine — collections with configurable vector size/distance, payload filtering (must/should/must_not), named and sparse vectors, hybrid search with prefetch and RRF/DBSF fusion, scalar/product/binary quantization, and the managed Qdrant Cloud with API-key/JWT auth and payload-based multitenancy.

Employees

~80

Industry

Vector Database

Headquarters

Berlin, Germany

Available eval packs for Qdrant

8 packs ready to run.

Why eval Qdrant AI

Qdrant's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.

The Corsac eval library for Qdrant measures four dimensions teams care about most when deploying ai platform agents:

  • Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
  • Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
  • Safety gates — does it escalate or refuse when it should, and only then?
  • Operator quality — does it preserve analyst trust by surfacing the right context at the right time?

Every eval pack above is hand-authored against Qdrant's public product surface and runnable in Corsac with your own data.