Simplify vector search implementation for complex AI applications
Superlinked helps AI engineers build better search and recommendation systems using both structured data and unstructured content. It solves real-world challenges like balancing search relevance with business objectives, making it essential for RAG systems and e-commerce recommendations. The Python framework simplifies deploying vector-based solutions across multiple use cases.
Combine text, images, and metadata into unified vectors
Balance relevance, freshness, and popularity in results
Manage vector compute layer through Python SDK
Same code works for experimentation and deployment
Combines text, images, and structured metadata into multi-modal vectors
Yes, designed for both experimentation and production deployment
Use the Python SDK available on GitHub or request a demo
This tool is best for:
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Superlinked can be integrated with these apps and services:
Recent data shows that Superlinked has 27.9K monthly visits (81.7% increase from the previous month), 50.0% bounce rate, and average 2.25 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 5 countries worldwide, led by United States contributing 26% of total traffic. SEO performance is shown by 5 tracked keywords, with "superlinked" being the top-performing keyword with 830 monthly searches. See below for more info.
Monthly Visits
27.9K
Pages per Visit
2.25
Bounce Rate
50.0%
Average Time on Site
48s
| SEO Keyword | Volume | CPC |
|---|---|---|
| superlinked | 830 | $3.05 |
| chromadb hybrid search | 180 | - |
| frontend word embedders website | 130 | - |
| vector db comparison | 120 | - |
| @superlinked | 110 | - |
| Source | Traffic Share |
|---|---|
| Direct | 40% |
| Search | 39% |
| Social | 7% |
| Referrals | 14% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 26% |
 | 15% |
 | 14% |
 | 13% |
 | 11% |
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