Malt super-powers freelancer recommendation with retriever-ranker architecture and Qdrant vector database
Malt's original monolithic matching model had response times of up to one minute and was inflexible, making it difficult to adapt for future large language models or scale to real-time recommendation needs.
After deploying the retriever-ranker architecture backed by Qdrant, p95 latency fell from tens of seconds (sometimes over a minute) to 3 seconds at most, and an AB test confirmed an increase in project conversion without sacrificing recommendation quality.
Frequently asked questions
What did this team achieve with this AI workflow?
After deploying the retriever-ranker architecture backed by Qdrant, p95 latency fell from tens of seconds (sometimes over a minute) to 3 seconds at most, and an AB test confirmed an increase in project conversion with…
What tools did this team use?
Qdrant, Kubernetes, Grafana, Prometheus, Docker.
What results were reported?
P95 response latency: 3 seconds at most; Project conversion: increase in the conversion of projects in our AB test (source-reported, not independently verified).
How is this recruiting AI workflow structured?
Project posted, vector encoded → Retriever narrows freelancer pool → Ranker scores and reorders subset → Near-real-time recommendations delivered.