Raw compute
for structured
intelligence

NēshaTech's innovative sharding process distributes AI workloads across GPUs, resolving raw compute into affordable, on-demand capacity for vectorization, inference, RAG and backend AI.

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Built for AI at Scale

Powerful infrastructure designed for the next generation of intelligent applications

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GPU Sharding

Distribute AI workloads seamlessly across multiple GPUs for maximum efficiency and throughput.

🔍

Vectorization

High-performance vector processing for embeddings, similarity search, and semantic analysis.

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Inference

Low-latency inference endpoints optimized for production AI workloads at any scale.

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RAG Support

Built-in retrieval-augmented generation capabilities for context-aware AI applications.

API Documentation

Simple, powerful APIs designed for developers. Integrate NēshaTech's compute infrastructure into your applications with just a few lines of code.

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# Initialize NēshaTech client import neshatech client = neshatech.Client("your-api-key") # Submit distributed inference job response = client.inference.create( model="nesha-large-v2", prompt="Process this data...", shards=4 ) print(response.result)

About NēshaTech

A lean operating team with expertise in AI infrastructure, enterprise-grade systems, and distributed networks.

Founded by Industry Veterans

NēshaTech is founded by Steve McAtee, prior CTO of Presearch. An innovative technology built to scale and bring a democratized AI to the masses. Our mission is to make high-performance AI compute accessible, affordable, and available on-demand for developers and enterprises worldwide.