Last week, I attended my first NVIDIA GTC event in Washington, DC, and it wasn’t just another industry conference. It felt like a turning point for enterprise AI, not because of a single announcement, but because everything across the keynotes, sessions, booths, and conversations pointed to the same shift: the next phase of AI won’t be won by bigger models. It will be won by better context. And this time, it wasn’t theoretical, it was visible.
The Moment the Shift Became Real
When Jensen Huang took the stage, he didn’t talk about what might be possible someday. He showed what’s already happening. One of the most striking moments was a live view inside an AI-powered factory — not a simulation, but a real production line with autonomous robots working, coordinating, and adapting in real time.
He then introduced something that didn’t get as many headlines, but may end up being the most important shift of all: a new class of “context processors.” These processors are designed for the next era of AI — where models don’t just respond to short prompts, but ingest and reason over large amounts of business context first. Instead of answering based on a single input, they allow AI systems to read hundreds of PDFs, analyze archived documents, parse video, or process multimodal data before generating a response. In other words: NVIDIA is now building infrastructure for AI that understands, not just predicts.
That marked a clear turning point — a shift from prompt-based AI to context-aware AI.
We’re moving from AI that produces answers to AI that understands business context, takes action, and delivers outcomes. And the competitive advantage won’t come from who has the biggest model — it will come from who gives AI the context and autonomy to operate inside the business.
Why Models Aren’t the Bottleneck Anymore
The challenge in most enterprises isn’t getting access to models anymore, it’s getting reliable, explainable, production-ready results from them. The real questions teams are asking now are:
- Why is the answer correct sometimes and wrong other times?
- Why can’t the AI explain how it got the answer?
- Why does the prototype work in a demo, but fail in production?
One day before GTC, OpenAI introduced apps in ChatGPT and a new Apps SDK, a new generation of apps you can chat with and tools for developers to build them. And Gartner is forecasting that by 2028, 15% of enterprise decisions will be made by AI agents—not co-pilots, not chatbots, but autonomous systems embedded in workflows. Which raises the real question: if AI is going to act, not just answer, what will it need? The answer running through GTC was clear: more business context.
Where AI Is Stalling: The Context Gap
Most AI today behaves like a brilliant intern—fast, capable, and able to generate impressive output, but still missing the business context and understanding that only comes from the relationships within enterprise data. It can answer questions, but it can’t understand how customers, products, SLAs, risks, compliance rules, or financial impact connect across the business.
And that’s exactly why so many GenAI pilots look promising, but never make it to production.
A Real Example: Why Output ≠ Resolution
For example, a model can summarize a single source of information, but it can’t connect what a support agent actually needs: the customer record in the CRM (relational), the product and contract data in the ERP (relational), the defect history in the engineering system (document + search), the workaround stored in the knowledge base (written + video), the warranty policy in a PDF, and the embedded vectors used for similarity search. Up until now, the only way to make this work has been to manually glue these systems together with pipelines, sync jobs, exports, and APIs, a setup that’s slow to build, expensive to maintain, and brittle the moment something changes in production.
And without true context across systems, formats, and data models, even an AI-assisted support agent—or an autonomous support bot—can respond, but it can’t resolve. The problem isn’t that enterprises lack data, it’s that they lack a unified business context layer that makes the data usable by AI.
Why This Is Leading Enterprises Toward Graph — But Not Graph Alone
Business context lives in relationships, which is why graph technology is becoming foundational for AI. But today’s AI workloads need more than a graph, they need vectors for embeddings, documents for unstructured content, key-value for fast lookups, search for retrieval, and multimodal inputs like video, audio, logs, and time-series.
You don’t get enterprise-grade AI by bolting a graph database onto a stack of disconnected systems. You get it by eliminating the stack entirely—unifying graph, vector, document, key-value, and search in one platform that already understands relationships across the data.
The next wave of AI isn’t about adding more disconnected data sources. It’s about giving AI the ability to understand the relationships within the data, because that’s what delivers true business understanding. Without those relationships, AI can see the data, but it can’t understand the business.
Introducing the Arango AI Data Platform
At the event, we announced the Arango AI Data Platform, a graph-powered, multi-model, multimodal data platform built for business context-driven AI. Built on top of our multi-model graph database (4.8 star rating on G2 for performance, flexibility and ease-of-use and 4.7 star rating on Gartner Peer Insights for our multi-model approach and lightening fast queries with one language across models, easy implementation and exceptional support), the new platform now includes:
- Graph + vector + document + key-value + search in one engine
- Multimodal ingestion: text, images, audio, video, logs, time-series, geospatial, embeddings
- Context-aware RAG and GraphRAG
- Natural language querying across all data models
- Bring-Your-Own-LLM (Gemini, ChatGPT, Claude, etc.)
- Built-in ML/AI pipelines, not bolt-ons
- GPU-accelerated scaling for enterprise workloads
Not another database. A unified business context layer for AI.
A Second Milestone: Our New Brand + Website
GTC wasn’t just a product launch for us, it was a company evolution. We announced the Arango AI Data Platform, introduced our new brand identity, and launched our new home at arango.ai.
The transition to Arango as our company name to make room for more products, expanding from ArangoDB to Arango AI Data Platform reflects what our customers are building now: context-aware, multi-model, and multimodal production-grade AI systems that can move from prototype to impact—without stitching together tools that were never designed to work together.
Yes, There Was a Robot Dog
From automated arms to four-legged robots and helpful humanoids, robots roamed the expo hall at NVIDIA GTC Washington, D.C. And yes, the Boston Dynamics robot dog was roaming the show floor. Not as a gimmick, but as evidence that AI has moved off screens and into physical systems. Robotics, autonomous systems, and agent-driven workflows are no longer experiments, they’re shipping.
The question now isn’t whether AI can act. It’s whether it understands the business before it does.
NVIDIA Partner Acknowledgements
We’re also honored to be a proud member of the NVIDIA Inception Program, which gives us early access to GPU innovations and a network of AI-driven startups aligned on the same mission.
- Joe Eaton – Distinguished System Engineer for Data & Graph Analytics, NVIDIA
Joe runs the Graph Center of Excellence at NVIDIA. His insights and feedback on our new product messaging. - Bradley Rees – Senior Manager, GPU-Accelerated Graph Analytics (RAPIDS), NVIDIA
Brad provided very valuable feedback on our new product messaging. - Bonita Bhaskaran – Director, DFX Methodology for Data Analytics & Applied AI, NVIDIA. She moderated an insightful panel discussion with some of our customers from Seimans and Synopsys on enterprise AI workflows and offered rich feedback on our product messaging.
- Jessica Clark – Senior Solutions Architect, GenAI, NVIDIA & Alexandrai Barghi – Software Engineer, NVIDIA
During their breakout session on improving fraud detection using Graph Neural Networks (GNNs), Jessica and Alexandrai showcased how graph-based methods can detect sophisticated fraud hidden from conventional systems. Their session drove a wave of visitors to our booth. Thanks for the shout out! - Stephen Bernstein – Account Manager, Strategic Startups (GenAI), NVIDIA
Stephen worked closely with us around strategic startups and ensured our launch aligned with NVIDIA’s GenAI ecosystem support. - Daniel Samhoun – Account Development, Generative AI Startups, NVIDIA
Daniel coordinated outreach across generative AI startups and helped facilitate our engagement in the NVIDIA AI ecosystem. - Liz Warner – Program Manager, NVIDIA Inception Partner Program
Liz supported our inclusion in the Inception Program and presence the the event.
