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The chart above demonstrates the substantial performance advantage of SARAHAI-INFERENCE, which can analyze up to 24 simultaneous Pattern-of-Life (PoL) video streams, compared to only 4 streams using traditional inference methods. This highlights SARAHAI's scalability and efficiency in real-time surveillance, edge analytics, and multimodal inference pipelines.
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​The chart shows the performance boost of SARAHAI-STORAGE for AI clusters, delivering 200,000 IOPS, which is 4x the performance of traditional storage solutions (50,000 IOPS). This enables significantly faster data ingestion, training set access, and model checkpointing, reducing I/O bottlenecks across high-performance AI pipelines.
The chart above compares the time required to complete various data science tasks using SARAHAI-LLM versus typical methods. It clearly illustrates how SARAHAI-LLM significantly accelerates workflows—reducing multi-hour tasks to nearly one or two hours, demonstrating its value in streamlining end-to-end data science operations.
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This chart showcases the superior responsiveness of SARAHAI-FACILITY for smart building automation, achieving an average response latency of just 100 milliseconds, compared to 800 milliseconds with traditional systems. This rapid response enables real-time optimization of HVAC, lighting, security, and occupancy management—dramatically improving energy efficiency and occupant experience.
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This chart illustrates the data throughput advantage of SARAHAI-NETWORK, which achieves 400 Gbps for NCCL or RCCL AI cluster traffic—four times the bandwidth of traditional networking methods operating at 100 Gbps. This substantial gain in throughput enables faster model training, reduced latency, and more efficient synchronization across distributed AI workloads
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This chart highlights the exceptional scalability of SARAHAI-IOT, which can process 1,000,000 MQTT messages per second—10x the throughput of traditional IoT handling methods. This makes it ideal for large-scale deployments across utilities, smart cities, and industrial environments requiring high-frequency sensor ingestion and real-time Pattern-of-Life analysis.
* Test Results based upon simulated data. Results may vary.
ABOUT US
Based in Silicon Valley, our team of creatives operates from the Plug and Play Accelerator in Sunnyvale, CA. We've developed a predictive AI platform on the foundation of tens of millions of dollars in R&D. For over five years, we've continuously refined this platform in collaboration with leading experts and institutions. Our mission is to empower Federal Departments, State and Local Agencies, Education, Utilities, and Telecom companies to deploy solutions that rapidly safeguard critical infrastructure from threats. Both internal and external.
