Behind the Scenes with NVIDIA: How DataQI is Evolving AI

A Trip to Silicon Valley

It is not every day that a software company receives a direct invitation to present at NVIDIA's headquarters.

DataQI engineers travelled to Silicon Valley to demonstrate the platform's on-premise AI capabilities, built on NVIDIA NIM microservices—NVIDIA's standardised, containerised inference engine for deploying production-grade large language models (LLMs). The visit provided direct access to NVIDIA's engineering leadership and the opportunity to validate DataQI's integration architecture against NVIDIA AI Blueprints.

The goal of the engagement was to demonstrate how DataQI deploys seamlessly within NVIDIA's accelerated infrastructure stack, and to explore the commercial and technical roadmap for joint enterprise deployments.

What is DataQI?

DataQI is an enterprise-grade Agentic AI platform that gives organisations structured, secure access to their institutional knowledge. Unlike conversational chatbots, DataQI automates multi-step workflows, generates intelligent documents, and integrates directly with existing enterprise data sources.

The platform supports on-premises, hybrid cloud, and fully cloud-hosted deployment models, with granular role-based access control designed for high-security industrial and regulated environments — including manufacturing, healthcare, and retail.

Day 1: Arriving in Silicon Valley

The DataQI team arrived in California and spent the evening in technical discussions, covering AI deployment architecture, enterprise data strategy, and the roadmap for NIM-powered deployments.

Day 2: Inside NVIDIA's HQ

At 7:15 AM, the DataQI team arrived at NVIDIA's Santa Clara headquarters. The programme opened with a series of sessions on high-performance computing (HPC), enterprise AI strategy, and the role of digital agents in operational automation. Key areas covered by the NVIDIA team included:

• NVIDIA DGX Cloud — A managed cloud platform for large-scale model training and fine-tuning, designed for enterprises requiring dedicated GPU capacity without on-premise infrastructure.
• NVIDIA Networking — High-bandwidth, low-latency InfiniBand and Ethernet interconnects underpinning AI cluster performance at scale.
• NVIDIA NIM Microservices — Containerised inference endpoints for LLMs, multimodal models, and domain-specific AI, demonstrated across factory automation, digital avatar, and protein simulation use cases.

The afternoon included a tour of NVIDIA's applied AI demo suite, covering live implementations across:

• Digital Humans for Customer Service — AI-generated avatars with real-time natural language interaction.
• Industrial Facility Digital Twins — Photorealistic factory simulation for operational planning and predictive maintenance.
• Generative AI Protein Design — Foundation model-powered molecular structure generation for life sciences.
• 3D Visual Conditioning — Spatial conditioning for precise, controllable generative output in design and engineering workflows.

Showcasing DataQI: Validated Enterprise Use Cases

Following NVIDIA's programme, the DataQI team presented 18 months of applied AI work across four validated enterprise use cases.

Computer Vision for Aerospace Quality Inspection

DataQI deployed machine learning and computer vision models to detect sub-visual surface defects in aerospace components — defects undetectable by standard human inspection. The system enabled consistent, automated quality assurance at production line speed. Read more in our computer vision in manufacturing resource.

Public Consultation Automation

DataQI applied generative AI to dramatically accelerate a process historically requiring weeks of manual analyst time. The system categorised, prioritised, and summarised high volumes of public consultation responses whilst maintaining human oversight and decision authority. This removed manual bottlenecks without replacing the analysts responsible for final judgements.

Quoting and Bid Automation

AI-driven automation of complex commercial pricing calculations and structured bid documentation, reducing project turnaround time for sales and estimation teams.

Voice and Telephone Customer Service

DataQI's AI-powered voice assistant was deployed for a transport operator to handle inbound customer queries, reducing call handling time and improving service availability outside business hours.

A key demonstration milestone was showing DataQI operating fully on-premise within an NVIDIA NIM environment. The NVIDIA engineering team validated the speed of integration and the platform's ability to surface measurable operational outcomes within enterprise constraints.

Next Steps: What the NVIDIA Engagement Delivers for DataQI Customers

The two-day engagement produced three concrete outcomes that directly advance DataQI's enterprise roadmap:

• NIM-Powered DataQI Deployment — DataQI will release its first production instance built on NVIDIA NIM, delivering improved inference speed, enhanced security isolation, and horizontal scalability for enterprise customers with high-throughput workloads.
• Access to NVIDIA Launchpad — DataQI secured access to over $250,000 in dedicated NVIDIA GPU compute via the NVIDIA Launchpad programme, accelerating model optimisation and performance benchmarking.
• Weekly Technical Cadence with NVIDIA Engineering — A standing weekly collaboration with NVIDIA's team ensures DataQI remains aligned with the latest NIM releases, AI Blueprint updates, and roadmap developments.

What the NVIDIA Partnership Means for Enterprise AI Deployment

The convergence of NVIDIA's GPU infrastructure, NIM's standardised inference architecture, and DataQI's enterprise AI platform creates a validated, production-ready stack for organisations deploying AI at scale.

For manufacturing, healthcare, and industrial operators, this means on-premise AI that meets data sovereignty requirements, performs at production throughput, and integrates with existing operational systems — without dependency on public cloud inference endpoints.

DataQI's NIM-powered deployment roadmap is now underway. Organisations evaluating enterprise AI infrastructure can speak with the DataQI team to understand how the NVIDIA-validated architecture applies to their specific operational environment.