Clinia has released the latest versions of our core health-grade retrieval models, with significant improvements that enable faster and more precise health information retrieval.

The Embedder and Reranker models are at the heart of Clinia’s health-grade platform, trusted by health innovators and product builders to deploy AI-powered experiences embedded inside their provider- and patient-facing applications. With this release, our partners can enhance clinical knowledge search workflows, delivering fast and accurate results to clinicians and researchers without adding infrastructure complexity.

Health-specific Models: Power More with Less

The Knowledge Embedder and Reranker are core models that power Clinia Platform’s text retrieval capability.

The new versions are trained on trusted health-specific data sourced from biomedical literature, combined with synthetic data generated through queries designed from a clinician’s perspective. This ensures our models can handle real-world health queries with greater accuracy and relevance.

Smarter Embedding for Better Retrieval

The Clinia Knowledge Embedder converts both documents and user queries into vectors. Documents are first encoded and stored in a vector database. When a query is made, it is also vectorized in the same way and compared against the vectors in the database. This process goes beyond simple keyword match and allows semantic search to capture nuances in user meaning and intent.

In healthcare, terminology is highly specialized, complex, and precise. And often the same concept can be expressed in many ways, from formal clinical terms to abbreviations and layperson language. This makes semantic understanding critical for ensuring completeness, so all relevant information is retrieved, regardless of how it’s phrased.

Compared to Clinia Knowledge Embedder V1, which was launched last November, the new model shows consistent improvements in accuracy across 10 medical specialties on the CURE benchmark, a health-specific dataset introduced and integrated into MTEB by Clinia. This rapid improvement reflects our continued focus on delivering quick and measurable enhancements for health-specific search.

When compared to models of similar size, Clinia Knowledge Embedder V2 shows 7.22% higher accuracy than the mGTE embedder, a model widely used as a reference point for performance.

It also reaches performance levels comparable to NV-Embed-V2, a leading LLM-based embedder that is 10 times larger.

Clinia Knowledge Embedder outperforms models of similar size and larger ones because our models are trained on verified data, validated by medical experts, and optimized for health-grade results. This also makes them practical for real-world deployment - by focusing exclusively on the health domain rather than every possible use case, we eliminate unnecessary complexity, resulting in a model that is lighter, faster, and easier to deploy.

Smaller but more efficient, this means health organizations and product teams of all sizes and resource levels can deliver high accuracy without the cost or latency of massive general models.

Sharper Reranking for The Most Relevant Results

Once the relevant data from the vector database is retrieved, the reranker decides what appears at the top. It analyses context, clinical relevance, and nuance to prioritize the results that truly matter.

Also purpose-built for health use cases, Clinia Knowledge Reranker V2 matches the performance of the industry-standard mGTE’s reranker and closes much of the gap with frontier LLM-based rerankers like Qwen3-Reranker.

Like our other health-specific models, this balance of performance and efficiency ensures product builders can achieve optimal ranking without the cost or complexity of massive models—making deployment easier and more efficient.

In real-world uses, this means Clinicians and researchers can get meaningful answers first, increasing efficiency and boosting confidence in every search interaction.

Crucial for Context-Powered Decision Workflows

In healthcare, fast and accurate access to information is critical for both humans and systems to act with clarity and confidence. Clinical applications such as Decision Support Tools and health knowledge platforms can not succeed without reliable search capabilities.

Product teams have turned to AI to power this critical workflow, yet generic AI tools often fall short when it comes to finding true semantic matches and highly relevant ranking for health terminology.

Resources and latency are also big pain points. Most frontier AI models aren’t designed for healthcare. To make them truly reliable for healthcare use cases, product teams often need to invest heavily in custom tuning. Also, larger models can introduce latency. Running them at scale is often impractical for real-time clinical environments.

Clinia AI solves these challenges by combining high-performance, health-specific models with a lean, scalable infrastructure. For health organizations building or scaling search-powered applications, it means faster deployment, higher user trust, and better ROI.

A Platform Ready for Real-World Scale

These new models are now fully integrated into Clinia’s Health-Grade APIs, powering key capabilities such as semantic search, generative question-answer, and conversational experiences.

Clinia remains focused on advancing health-grade AI that excels in real healthcare environments. Future releases will continue expanding specialty coverage and pushing the boundaries of health-grade intelligence—helping organizations deliver better information access, faster decisions, and improved care outcomes at scale.