COO
Vesiro
Vesiro provides a next-generation search engine built on Opensearch, utilizing patented technology to enhance full-text search performance. It enables companies to achieve up to 2X faster search results and a 46% reduction in server energy consumption, optimizing resource use and reducing operational costs.
- Artificial Intelligence
- Data & Analytics
- Enterprise Software
- Software Only
Updated 13 days ago
Funding
Raised to date
$530KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Funding rounds are not available yet.
Founders
Product
Problem
Existing full-text search solutions can be slow and resource-intensive, leading to high operational costs and energy consumption for companies managing large volumes of data. Optimizing search performance often requires significant changes to indexing strategies or query structures, adding complexity and overhead.
Solution
Vesiro offers a search engine built on OpenSearch that enhances full-text search performance without requiring changes to existing indexing or query methods. Vesiro's patented technology delivers faster search results while significantly reducing server energy consumption. This allows companies to optimize resource utilization, lower operational expenses, and scale their search capabilities more efficiently.
Target Audience
Vesiro targets companies that rely on full-text search and are looking to improve performance, reduce operational costs, and optimize resource utilization, including those using OpenSearch or Elasticsearch.
Features
- Patented technology for enhanced full-text search performance
- Drop-in replacement for OpenSearch, requiring no changes to indexing or query structure
- Up to 2x faster search results compared to Elasticsearch
- Up to 46% reduction in server energy consumption
- Optimized resource utilization for more efficient cluster performance
- Economical scaling, allowing for increased capacity from existing clusters or reduced cluster size for cost savings