# WEKA® NeuralMesh™ AI Data Platform

**Accelerating AI Inference Pipelines at Enterprise Scale**

The easiest way to keep enterprise data prepared, current, and accessible, so you can deploy and scale your AI factory.

## Engineered to Deliver What Production AI Demands

### Semantic Search

**Semantic Search**

Natural language queries over enterprise NeuralMesh content, respecting POSIX ACLs. Delivered as a ready-to-use application.

### Video Search & Summarization

**Video Search & Summarization**

NVIDIA Video Search & Summarization (VSS) integration for video intelligence on content stored in NeuralMesh.

### OpenFold / AlphaFold

**OpenFold / AlphaFold**

AI pipeline for drug discovery and life sciences research via protein structure prediction methods.

### AIQ / Agentic RAG

**AIQ / Agentic RAG**

Multi-step agentic retrieval-augmented generation for complex enterprise queries.

## Results. Not Promises.

- **6x** *Data reduction with under 5% write overhead*
- **3x** *Greater object throughput than nearest alternatives*
- **90%** *Lower costs than traditional storage solutions*

## Customer Story

> NeuralMesh’s architecture is built specifically for GPU-driven, parallel AI workloads. [It] has been rock-solid, allowing our team to focus on building AI infrastructure rather than troubleshooting storage issues. That reliability gives us the confidence to take on our most demanding customers.

— Anindya Das, Co-founder and CTO • Neysa

## Frequently Asked Questions

### What is an AI data platform?

An AI data platform turns stored data into AI-ready context: parsing, chunking, embedding, indexing, serving it with permissions intact. Storage holds data. A platform prepares it. WEKA built on NVIDIA's reference design and shipped at GTC 2026.

### Why do most enterprise AI projects stall between pilot and production?

Pilots run on a static snapshot. Production needs the pipeline current as files change, with permissions on every answer. Most teams rebuild it per project. That is where AI stacks hit a wall, and it is an architecture problem, not a model problem.

### What does an AI factory need from its data layer?

Four things: throughput to feed GPUs, continuous data movement, a persistent context tier, and governance end to end. Deliver only throughput and you bottleneck the rest. WEKA builds all four into AI factories on NVIDIA reference architectures.

### How do you keep a vector database in sync with a filesystem?

Event-driven propagation, not batch jobs. Every create, update, and delete must reach the index or models cite data that no longer exists. NeuralMesh diffs tracked paths continuously: no scheduler, no polling, no reindex window. Architecture detail.

### How do you enforce access controls in a RAG pipeline?

Capture permissions at ingest, enforce them at query. Most pipelines strip ACLs during embedding, so the vector store cannot tell who may see what. WEKA embeds POSIX ACLs in the record: cannot see the file, cannot see the vector. Watch it enforced.

### What is the difference between AI storage and an AI data platform?

Storage answers where data lives and how fast it moves. A platform answers whether a model can use it now. That gap is why storage became the inference battleground. Being software-defined, WEKA runs natively on your GPU servers.

### How long does it take to build an AI data pipeline?

Assembled from parts: vector database, embedding model, chunker, retrieval framework, security layer, revalidated per project. Blueprints collapse it to one step with NVIDIA NIM and NeMo Retriever built in, taking deployment from months to minutes.

### Do you need dedicated GPUs to prepare data for AI?

Yes. Embedding, re-ranking, and multimodal parsing are GPU work, and running them on training GPUs steals cycles from the models that justified the cluster. WEKA runs it on NVIDIA RTX PRO 6000 and RTX PRO 4500 Blackwell, following NVIDIA STX.

### How much extra storage does vectorization consume?

Plan for 3-4x inflation on text-dense data. A 200TB corpus of logs and documents can occupy 500-800TB once chunks, embeddings, and indexes land. Budgets miss this, and flash prices are up 60%. Density matters: see WEKApod 3.

### Why does long-context and agentic inference get more expensive at scale?

KV-cache gets recomputed instead of reused. Every extra turn, longer prompt, and added agent re-derives context the GPU already produced, raising cost per token. Augmented Memory Grid extends KV-cache 1000x, delivering 4.2x more tokens per GPU.

## Related Resources

- [Unlock AI Outcomes with NeuralMesh AIDP](/resource/unlock-ai-outcomes-with-neuralmesh-aidp) (Demo)
- [NeuralMesh AI Data Platform (AIDP)](/resource/neuralmesh-ai-data-platform-aidp) (Solution Brief)
- [NeuralMesh AIDP in Healthcare and Life Sciences](/resource/neuralmesh-aidp-healthcare-and-life-sciences) (Solution Brief)
- [NeuralMesh AIDP in Financial Services](/resource/neuralmesh-aidp-financial-services) (Solution Brief)