JACKSONVILLE, Fla., June 10, 2026 /PRNewswire/ -- ELEGRP, the world-leading manufacturer of intelligent electrical and electromechanical equipment, announced the launch of its humidity sensor and fan control switch, designed to automatically manage indoor moisture levels and improve ventilation in bathrooms, laundry rooms, basements, and other humidity-prone spaces.
The new product series includes both a standalone humidity sensor and fan control switch and a combination model with integrated light control, providing homeowners, builders, and contractors with flexible solutions.
Featuring a digital humidity sensor and microprocessor-based control technology, the devices continuously monitor ambient humidity levels and automatically activate connected ventilation fans when excess moisture is detected. By helping reduce condensation, mold, and mildew, the controls contribute to improved indoor air quality and everyday comfort while minimizing false activations commonly associated with conventional humidity-sensing devices.
Easy Setup Without Removing the Wall Plate
Designed for ease of use, the humidity sensor controls allow users to adjust humidity levels and fan run times directly from the device—no wall plate removal or specialized tools required. This streamlined setup simplifies both installation and ongoing adjustments, making it more convenient for homeowners and professional electricians.
Intelligent Ventilation with Multiple Operating Modes
To support different ventilation needs throughout the home, the product series offers three operating modes：
- Bath Fan Mode: Automatically turns on the ventilation fan when humidity rises above the selected level and runs for a preset duration.
- Air Cycle Mode: Provides scheduled ventilation to maintain consistent air circulation and ensure fresh indoor air quality.
- Humidistat Mode: Activates the fan when humidity exceeds the set threshold, runs for a preset period, and then remains off for the rest of the cycle.
The humidity sensor fan control supports ventilation fans up to 1/4 horsepower. The combination model also features integrated lighting control and is compatible with a wide range of bulbs, delivering a flexible, all-in-one solution for modern home environments.
"Proper ventilation is essential for a healthy and comfortable living environment," said Jonson, Product Manager at ELEGRP. "With building standards evolving and growing industry focus on indoor air quality, California has already implemented strict ventilation requirements, and we expect more U.S. states to roll out similar regulatory requirements down the line. We developed this product series to deliver simple, reliable and intelligent humidity control. Our core goal is to streamline installation and daily operation without compromising performance, meeting both the compliance and performance requirements of modern residential projects."
The ELEGRP Humidity Sensor and Fan Control series is certified to UL 60730 and CSA C22.2 No. 60730 standards and is designed to support compliance with California Title 24 (2019 and 2022 editions), CAL Green indoor air quality requirements, and ASHRAE 62.2 residential ventilation standards. The devices are now available on Amazon.
About ELEGRP
Founded in 2000, ELEGRP is a world-leading manufacturer of intelligent electrical and electromechanical equipment, including residual-current devices, wiring devices, smart home devices, wires and cables, domestic water pumps and more. Its products are widely used in household appliances, personal care appliances, smart bathrooms, power equipment, and building electrical systems.
ELEGRP now operates in more than 40 countries and regions worldwide. With a user-centered approach, ELEGRP commits to improving electrical appliance safety, expanding smart appliance and smart home applications, and providing users around the world with safer and more convenient products, services, and solutions.
For more information, please visit https://www.elegrp.com/ and follow ELEGRP on LinkedIn, Instagram, Facebook, Reddit, and YouTube.
** This press release is distributed by PR Newswire through automated distribution system, for which the client assumes full responsibility. **
ELEGRP Launches New Humidity Sensor and Fan Control Switch to Improve Indoor Air Quality and Residential Ventilation
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Available now in public preview on Zilliz Cloud, Vector Lakebase keeps production vector search at its core and adds shared lake-native storage and on-demand compute — bringing real-time serving, interactive discovery, and batch analytics onto one data foundation.
REDWOOD CITY, Calif., June 10, 2026 /PRNewswire/ -- Zilliz, the company behind Milvus, the world's most widely adopted open-source vector database, today announced the public preview of Zilliz Vector Lakebase, a major Zilliz Cloud release that pairs the production vector database with a shared, lake-native data foundation.
Vector Lakebase keeps Zilliz Cloud's real-time vector search at the core — the engine Zillow, OpenEvidence, Exa, Filevine, MiniMax, and more than 10,000 enterprises and AI teams already rely on — and extends it with three new ways to operate on the same data: interactive discovery, large-scale batch analytics, and search directly on external data lakes. The result is a single data foundation in which every workload runs against a single logical copy of the data, with on-demand and batch jobs billed only when compute is active.
"Production vector search is and will remain at the heart of what Zilliz does — it's why thousands of teams choose Milvus and Zilliz Cloud, and it's getting faster and more cost-efficient every release," said Charles Xie, Founder and CEO of Zilliz. "Vector Lakebase is what we believe comes next: one data foundation where the same vectors can serve a production query, anchor a discovery session, and power a multi-petabyte training-data pipeline — without copies, migration, or a parallel stack."
Why a Single Data Foundation Matters
AI systems are no longer a single-query retrieval problem. They run as a continuous loop — serve, learn from feedback, mine and prepare better data, then serve again — and each turn typically requires separate systems for serving, exploration, and large-scale processing. Moving billions of vectors between those systems can take days. The cost and complexity are so high that many teams skip the loop altogether, leaving valuable data retrievable but never improved.
Vector Lakebase closes that gap with a zero-copy semantic data plane on shared lake-native storage: real-time serving, interactive discovery, and batch analytics all run against one logical copy of the data, scaling from gigabytes to petabytes.
"Teams asked for a way to keep their data in one place and run very different workloads against it — from real-time agent memory to overnight semantic deduplication," said Robert Guo, VP of Product at Zilliz and one of the architects behind Milvus. "Vector Lakebase delivers that through a unified storage layer on Vortex, tiered serving for the production path, and on-demand compute for everything else."
Five Capabilities on One Foundation
- Tiered Real-Time Serving. Three production tiers tuned for different workloads: Performance-Optimized (1,000+ QPS, single-digit-millisecond latency, in-memory); Capacity-Optimized (100–500 QPS, sub-100ms latency, memory + NVMe); and Tiered-Storage (10–50 QPS, ~100ms latency, spanning memory, NVMe, and object storage at significantly lower cost). All tiers default to 95–98% recall, tunable to 99%+, backed by Zilliz Cloud's 99.99% uptime SLA and Global Cluster cross-region high availability.
- On-Demand Search. Pay-as-you-go compute for workloads where infrastructure sits idle most of the time, billed directly for object storage and compute rather than serverless markups. In Zilliz's internal benchmark on one billion 768-dimension vectors with 10 hours of monthly active compute, On-Demand Search totaled $318 versus $4,937 for a comparable serverless path — roughly 1/15 the cost.
- External Data Lake Search. A zero-copy External Collection mode that adds state-of-the-art indexing and full-spectrum search directly to existing Lance, Iceberg, Parquet, and Vortex tables, with incremental sync on refresh. Source data stays where it lives.
- Full-Spectrum AI Search. Search across vectors (dense and sparse), text, JSON, and geospatial data, with hybrid retrieval, BM25, regex, multi-vector and iterative search, and multi-path retrieval. Results can be reranked with Cohere, Voyage AI, RRF, and weighted/boost/decay strategies.
- Unified Lake-Native Storage. Shared storage for serving and analytics built on Vortex, an open columnar format designed for faster, cheaper random reads than Lance and Parquet, paired with object-storage-aware indexes (vector, BM25, JSON) that cut read amplification by over 90%. A 100-million-row schema backfill typically completes in single-digit minutes — without disrupting active query traffic.
Together, these capabilities let AI teams consolidate what previously required parallel always-on serving clusters and separate batch systems onto one platform — with consistent indexes, versioned data, and compute that scales to zero between jobs.
Availability
Zilliz Vector Lakebase is available now in public preview on Zilliz Cloud, alongside Serverless, Dedicated, and BYOC deployment options across more than 30 regions on AWS, Google Cloud, and Microsoft Azure. New work email signups receive $100 in free credits at zilliz.com. Teams running serving, discovery, and analytics on separate stacks can contact the Zilliz team for a tailored walkthrough.
About Zilliz
Zilliz is a leading AI data infrastructure company and the creator of Milvus, the world's most widely adopted open-source vector database, with 44,000+ GitHub stars and over 100 million Docker pulls. Zilliz helps enterprises and AI startups make their unstructured data searchable, analyzable, and governable — turning text, images, audio, video, and more into a strategic asset for production AI.
Zilliz's technology centers on Milvus and Zilliz Cloud. Milvus is an open-source vector database purpose-built for 100-billion-scale vector search. Zilliz Cloud extends that foundation into a fully managed Vector Lakebase platform, combining the high-throughput, low-latency serving capabilities of vector databases with the openness, scalability, and economics of multimodal data lakes. Zilliz powers more than 10,000 enterprises and AI-native startups worldwide, including MiniMax, OpenEvidence, Filevine, Exa, Salesforce, and Read AI.
Headquartered in Redwood Shores, California, Zilliz is backed by leading investors, including Aramco's Prosperity 7 Ventures, Temasek's Pavilion Capital, Hillhouse Capital, 5Y Capital, Yunqi Partners, and Trustbridge Partners. Learn more at Zilliz.com.
Available now in public preview on Zilliz Cloud, Vector Lakebase keeps production vector search at its core and adds shared lake-native storage and on-demand compute — bringing real-time serving, interactive discovery, and batch analytics onto one data foundation.
REDWOOD CITY, Calif., June 10, 2026 /PRNewswire/ -- Zilliz, the company behind Milvus, the world's most widely adopted open-source vector database, today announced the public preview of Zilliz Vector Lakebase, a major Zilliz Cloud release that pairs the production vector database with a shared, lake-native data foundation.
Vector Lakebase keeps Zilliz Cloud's real-time vector search at the core — the engine Zillow, OpenEvidence, Exa, Filevine, MiniMax, and more than 10,000 enterprises and AI teams already rely on — and extends it with three new ways to operate on the same data: interactive discovery, large-scale batch analytics, and search directly on external data lakes. The result is a single data foundation in which every workload runs against a single logical copy of the data, with on-demand and batch jobs billed only when compute is active.
"Production vector search is and will remain at the heart of what Zilliz does — it's why thousands of teams choose Milvus and Zilliz Cloud, and it's getting faster and more cost-efficient every release," said Charles Xie, Founder and CEO of Zilliz. "Vector Lakebase is what we believe comes next: one data foundation where the same vectors can serve a production query, anchor a discovery session, and power a multi-petabyte training-data pipeline — without copies, migration, or a parallel stack."
Why a Single Data Foundation Matters
AI systems are no longer a single-query retrieval problem. They run as a continuous loop — serve, learn from feedback, mine and prepare better data, then serve again — and each turn typically requires separate systems for serving, exploration, and large-scale processing. Moving billions of vectors between those systems can take days. The cost and complexity are so high that many teams skip the loop altogether, leaving valuable data retrievable but never improved.
Vector Lakebase closes that gap with a zero-copy semantic data plane on shared lake-native storage: real-time serving, interactive discovery, and batch analytics all run against one logical copy of the data, scaling from gigabytes to petabytes.
"Teams asked for a way to keep their data in one place and run very different workloads against it — from real-time agent memory to overnight semantic deduplication," said Robert Guo, VP of Product at Zilliz and one of the architects behind Milvus. "Vector Lakebase delivers that through a unified storage layer on Vortex, tiered serving for the production path, and on-demand compute for everything else."
Five Capabilities on One Foundation
- Tiered Real-Time Serving. Three production tiers tuned for different workloads: Performance-Optimized (1,000+ QPS, single-digit-millisecond latency, in-memory); Capacity-Optimized (100–500 QPS, sub-100ms latency, memory + NVMe); and Tiered-Storage (10–50 QPS, ~100ms latency, spanning memory, NVMe, and object storage at significantly lower cost). All tiers default to 95–98% recall, tunable to 99%+, backed by Zilliz Cloud's 99.99% uptime SLA and Global Cluster cross-region high availability.
- On-Demand Search. Pay-as-you-go compute for workloads where infrastructure sits idle most of the time, billed directly for object storage and compute rather than serverless markups. In Zilliz's internal benchmark on one billion 768-dimension vectors with 10 hours of monthly active compute, On-Demand Search totaled $318 versus $4,937 for a comparable serverless path — roughly 1/15 the cost.
- External Data Lake Search. A zero-copy External Collection mode that adds state-of-the-art indexing and full-spectrum search directly to existing Lance, Iceberg, Parquet, and Vortex tables, with incremental sync on refresh. Source data stays where it lives.
- Full-Spectrum AI Search. Search across vectors (dense and sparse), text, JSON, and geospatial data, with hybrid retrieval, BM25, regex, multi-vector and iterative search, and multi-path retrieval. Results can be reranked with Cohere, Voyage AI, RRF, and weighted/boost/decay strategies.
- Unified Lake-Native Storage. Shared storage for serving and analytics built on Vortex, an open columnar format designed for faster, cheaper random reads than Lance and Parquet, paired with object-storage-aware indexes (vector, BM25, JSON) that cut read amplification by over 90%. A 100-million-row schema backfill typically completes in single-digit minutes — without disrupting active query traffic.
Together, these capabilities let AI teams consolidate what previously required parallel always-on serving clusters and separate batch systems onto one platform — with consistent indexes, versioned data, and compute that scales to zero between jobs.
Availability
Zilliz Vector Lakebase is available now in public preview on Zilliz Cloud, alongside Serverless, Dedicated, and BYOC deployment options across more than 30 regions on AWS, Google Cloud, and Microsoft Azure. New work email signups receive $100 in free credits at zilliz.com. Teams running serving, discovery, and analytics on separate stacks can contact the Zilliz team for a tailored walkthrough.
About Zilliz
Zilliz is a leading AI data infrastructure company and the creator of Milvus, the world's most widely adopted open-source vector database, with 44,000+ GitHub stars and over 100 million Docker pulls. Zilliz helps enterprises and AI startups make their unstructured data searchable, analyzable, and governable — turning text, images, audio, video, and more into a strategic asset for production AI.
Zilliz's technology centers on Milvus and Zilliz Cloud. Milvus is an open-source vector database purpose-built for 100-billion-scale vector search. Zilliz Cloud extends that foundation into a fully managed Vector Lakebase platform, combining the high-throughput, low-latency serving capabilities of vector databases with the openness, scalability, and economics of multimodal data lakes. Zilliz powers more than 10,000 enterprises and AI-native startups worldwide, including MiniMax, OpenEvidence, Filevine, Exa, Salesforce, and Read AI.
Headquartered in Redwood Shores, California, Zilliz is backed by leading investors, including Aramco's Prosperity 7 Ventures, Temasek's Pavilion Capital, Hillhouse Capital, 5Y Capital, Yunqi Partners, and Trustbridge Partners. Learn more at Zilliz.com.
** This press release is distributed by PR Newswire through automated distribution system, for which the client assumes full responsibility. **
Zilliz Launches Vector Lakebase, Extending the World's Most Adopted Vector Database into a Unified Data Platform for AI
