Generative AI is rapidly transitioning from experimentation to enterprise-scale adoption. Organizations are investing in large language models (LLMs), advanced infrastructure, and AI -driven applications. However, many initiatives struggle to move beyond proof-of-concept due to challenges in data integration, scalability, and cost efficiency.
Key Challenges that inhibit adoption of GenAI in the enterprise
- Access to high-quality, multimodal enterprise data
- Complexity in building scalable, reliable and real-time data pipelines
- Driving ROI with large and expensive models (40B–80B parameters)
- Operational hurdles related to optimization and security
The Gap
The real challenge isn’t access to LLMs — it’s achieving consistent accuracy on business-specific data in a way that is secure, scalable, and operationally repeatable. In practice, enterprises typically need a combination of Retrieval-Augmented Generation (RAG) and fine-tuned models to get there.
- RAG (Retrieval-Augmented Generation) provides responses based on enterprise content. This can greatly improve relevance and reduce mistakes. However, production-grade RAG is not as simple as just adding a vector database. It needs clean and current source data, effective chunking and metadata, high-quality embeddings, tuned retrieval and reranking, permissions-aware access control, evaluation, and ongoing monitoring. Without these elements, retrieval can become noisy, slow, and difficult to manage.
- Fine-tuning boosts accuracy and consistency for specific language, policies, and workflows by teaching the model how your business communicates and makes decisions. However, it is also not straightforward. It requires well-defined tasks, curated and labelled datasets, careful data management to avoid training on sensitive or low-quality content, tracking experiments, and managing compute budgets. It also adds lifecycle needs like versioning, regression testing, and retraining as the business evolves.
The opportunity — and the gap — is that RAG and fine-tuning are complementary: RAG supplies up-to-date, permissioned business facts, while fine-tuning encodes stable domain behaviour (terminology, formats, rules, and decision logic). Combining them well requires a structured approach that defines what should be retrieved vs. generated, establishes shared data governance, and measures quality end-to-end (retrieval + generation) so accuracy improves without increasing cost, latency, or risk.
A Structured Approach to GenAI in the enterprise
To address these challenges head-on, DataNeuron and Google Cloud NetApp Volumes aka NetApp Volumes jointly deliver a unified approach to enterprise GenAI — combining data access, retrieval intelligence, and model customization.
NetApp Volumes provides scalable, high-performance and secure access to enterprise data, while DataNeuron builds intelligent pipelines on top of it, enabling controlled retrieval, model tuning, and end-to-end GenAI workflows.
Together, we focus on solving three critical problems:
- RAG with Access Control (ACL)
Ensures responses are generated only from authorized enterprise data, enforcing fine-grained access control and improving security and governance. - Optimized Document Retrieval
Narrows retrieval to relevant data sources, improving accuracy while reducing latency and compute overhead. - Fine-Tuning with Enterprise Data
Enables fine-tuning on curated enterprise datasets to build domain-specific models, improving relevance and overall model performance.
Core Capabilities
RAG with Access Control (ACL)
Traditional RAG systems often lack the ability to enforce access control boundaries, creating risks in enterprise environments. DataNeuron addresses this by embedding role-based and document-level access control directly into the retrieval pipeline, ensuring responses are generated only from permissible data sources at query time. Through the integration with NetApp Volumes, customers would be able to segment their data in file shares into multiple secure tenants and extend their security posture while building data pipelines for GenAI with improved governance, reduced risk, and greater trust in AI-generated outputs.
Optimized Document Retrieval
Full-corpus retrieval in RAG pipelines often introduces latency, increases processing duration, dilutes context with irrelevant information and degrades response quality. DataNeuron enables targeted retrieval by restricting queries to relevant datasets or documents, thereby improving precision and efficiency. For customers of NetApp Volumes, this would mean that they can achieve higher response accuracies based on their data with minimal performance overhead on the storage system which leads to faster response times, optimized resource utilization and more consistent, reliable outputs.
Fine-Tuning on Enterprise Data
While large foundation models offer broad capabilities, they are not ready to take on enterprise use cases where there is a need for domain specific adaptation, which makes fine-tuning essential for improving relevance and output quality and relate to an organization’s requirement. DataNeuron’s DSEAL is a key differentiator, which intelligently curates high-value, representative datasets, reduces noise and improves training efficiency. This enables organizations to build smaller, domain-aligned models with better performance, lower compute costs, and faster time-to-value. In-place fine-tuning with the enterprise data in NetApp Volumes, eliminates the need for data movement and combined with the custom-performance feature of NetApp Volumes, customers can dynamically leverage anywhere from 64 MiB/s to 22 GiB/s of throughput to accelerate fine-tuning.
Other Integrations with NetApp
Through integrations with NetApp technologies such as ONTAP snapshots, Instaclustr, and NetApp Console, DataNeuron enables reliable data versioning, managed open-source infrastructure, and protection of sensitive data through PII masking.
Together, they simplify operations while providing a secure, scalable foundation that accelerates enterprise GenAI adoption.
Bringing It All Together
Successful enterprise GenAI requires seamless integration of data, security, and workflows. DataNeuron and Google Cloud NetApp Volumes make this possible by combining controlled data access, precise retrieval, and domain-aligned models to deliver scalable, production-ready GenAI with better performance and cost efficiency.
