Domain-specific embeddings
LoRA embedding API.
Production embeddings fine-tuned with LoRA for specialized domains. Up to 14.5× better retrieval than base models on cardiology, legal, and medical applications. Real benchmarks on /demo/cardioembed.
Three reasons · 3
Why LoRA.
Performance
Up to 1452% improvement.
LoRA fine-tuning delivers up to 14.5× better retrieval accuracy versus general-purpose embeddings — measured on real cardiology benchmarks.
- Dramatically improves RAG system relevance
- Better semantic search for clinical documents
- More accurate similarity matching for decision support
- Small adapter, large gains — cost-effective
Portability
Model agnostic.
Portable LoRA adapters work with Qwen, Gemma, E5, BioLinkBERT, or your own base. No vendor lock-in.
- Data sovereignty — run on-premise with your model of choice
- Regulatory compliance — meets HIPAA and GDPR requirements
- No vendor lock-in — switch base models anytime
- Air-gapped deployments — fully offline capable
Operations
Production-ready.
OpenAI-compatible API on H100 GPUs. Sub-50ms latency, drop-in replacement for existing embedding code.
- 99.9% uptime SLA
- Auto-scaling infrastructure
- Batch processing support
- Enterprise support available
Available models · 4
The lineup.
Each model is a LoRA adapter trained on a domain-specific corpus. Portable across compatible base models, deployable on your own infrastructure or via the hosted API.
CardioEmbed
Cardiology
Portable LoRA adapter for clinical cardiology. Adapts general-purpose models to understand echocardiograms, clinical notes, and cardiology terminology.
Compatible base models
Improvement
14.5×
Accuracy
99.6%
| Model | Sep. | Improv. | Dim |
|---|---|---|---|
| CardioEmbed Qwen3-8B | 0.510 | +1452% | 3584 |
| Gemma-2-2B | 0.455 | +700% | 2304 |
| Qwen3-4B | 0.446 | +1184% | 1792 |
| Qwen2.5-0.5B | 0.327 | +1215% | 896 |
| E5-large-v2 | 0.284 | +787% | 1024 |
| BioLinkBERT-Large | 0.168 | +438% | 1024 |
OncoEmbed
Oncology
Oncology research adapter. Interprets cancer pathology reports and clinical-trial protocols across standard open-weights models.
Compatible base models
Improvement
7.0×
Accuracy
TBD
PV / Safety
Pharmacovigilance
Drug-safety adapter for adverse event reporting and signal detection. Compatible with major open-weights embedding models.
Compatible base models
Improvement
TBD
Accuracy
TBD
LegalEmbed
Legal
Legal-document embeddings for case law, contracts, and regulatory compliance. Trained on legal precedents and statutes.
Compatible base models
Improvement
6.5×
Accuracy
TBD
Quick start
OpenAI-compatible.
Drop-in API. If you can call OpenAI's embedding endpoint, you can call this one.
Get vector representations for your text using a domain-tuned model.
import requests
API_URL = "https://deepneuro.ai/lora-api/v1/embeddings"
API_KEY = "YOUR_API_KEY"
response = requests.post(
API_URL,
headers={"X-API-Key": API_KEY},
json={
"input": "Patient presents with acute myocardial infarction",
"model": "cardioembed-qwen3-8b",
},
)
data = response.json()
embedding = data["data"][0]["embedding"] # 3584-dim vector
print(f"Embedding dimensions: {len(embedding)}")Try it live
CardioEmbed in your browser.
Generate an embedding directly. The serverless backend may take 10-30 seconds to warm up on the first request after idle.
Pricing
Pay only for use.
No hidden fees, no minimum commitments, no enterprise sales calls.
Related research
Where this comes from.
- CardioEmbed: Domain-Specialized Text Embeddings for Clinical Cardiology — arXiv:2511.10930
- Comparative Analysis of LoRA-Adapted Embedding Models — arXiv:2511.19739
- LoRA: Low-Rank Adaptation of Large Language Models — arXiv:2106.09685
- The full research practice → /research
- Benchmark visualization → /demo/cardioembed