Semantic search across documents. Named entity recognition. Question answering from text. Three powerful models, one flat rate. $19/mo, unlimited. No APIs, no Docker, no GPU bills.
Semantic search • Named Entity Recognition • Extract answers • Unlimited requests
384-dimensional vectors that actually understand meaning.
Search by meaning, not keywords. Find relevant documents even when exact terms don't match. Perfect for knowledge bases and documentation.
Compare any two pieces of text and get a similarity score. Detect duplicates, find related content, or cluster similar items together.
Group similar documents, support tickets, or feedback automatically. No training data needed. Just embed and cluster.
Extract answers directly from paragraphs. Paste context, ask a question, get the answer with confidence scores. No training needed.
Extract people, organizations, locations, and other entities from text. Identify key information automatically with confidence scores.
Recommend articles, products, or courses based on semantic similarity. "If you liked this, you'll love this."
Generate embeddings for your retrieval-augmented generation pipeline. Embed documents, store vectors, retrieve context.
Create an account and subscribe for $19/mo. Takes 60 seconds. Start searching immediately.
Enter your text or upload multiple sentences. The model processes up to 100 texts at once.
Receive 384-dimensional vectors in milliseconds. Use them for search, similarity, clustering, or any NLP task.
No tiers, no gotchas, no surprise bills.
Everything you need for semantic search.
sentence-transformers/all-MiniLM-L6-v2 from Hugging Face. It produces 384-dimensional dense vectors optimized for semantic similarity. It's the most popular open-source embedding model with 50M+ downloads.
Yes. Flat $19/mo for unlimited requests. We use Hugging Face Inference API which handles the compute. No metered billing, no per-token charges, no surprise invoices.
OpenAI's text-embedding-3-small costs $0.02/1M tokens and produces 1536-dim vectors. MiniLM produces 384-dim vectors which are faster to compute and compare. For most use cases (search, clustering, similarity), MiniLM is more than enough and significantly cheaper at scale.
Absolutely. The model is battle-tested with 50M+ downloads. We provide reliable uptime backed by Hugging Face's infrastructure. Use it for search, recommendations, RAG, whatever you need.
No. Log in, subscribe, use the dashboard. That's it. No API key rotation, no secret management, no environment variables. We handle everything.
Email support@polsia.com. We typically respond within a few hours. If you're building something cool with our embeddings, we want to hear about it.
$19/mo. Unlimited semantic search. Start building in 60 seconds.
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