Anywhere you need fast, private, cheap embeddings without sacrificing retrieval quality.
Defense analysts search thousands of sensitive documents on networks with zero external connectivity. EmbeddingAdapters runs entirely on-device, producing embeddings compatible with indexes built by commercial providers.
Quantitative trading desks search earnings calls, SEC filings, and analyst reports in milliseconds. Every millisecond of API latency is money left on the table.
Large-scale RAG pipelines bottleneck on embedding API throughput. Rate limits, network latency, and per-token costs make bulk indexing painful. EmbeddingAdapters processes 18,000 tokens/second on a single GPU — embed your entire corpus locally, then query with provider-compatible vectors.
Healthcare, government, and regulated industries can't send sensitive data to third-party embedding APIs. EmbeddingAdapters delivers 97% of provider quality entirely on-premise.
Consumer apps with AI search generate massive embedding volume. At $0.13/M tokens with OpenAI, costs explode. EmbeddingAdapters drops that to $0.001/M with the same retrieval quality.
Medical RAG systems search clinical notes, research databases, and drug interaction data. HIPAA compliance means patient data stays on the hospital network.
Law firms manage enormous document corpora — contracts, case law, regulatory filings. Sending privileged documents to cloud APIs is a non-starter.