Find out about an AI-powered expertise launched at least viable product to collect actual consumer suggestions and iterate quickly.
After we speak about agentic AI, it’s simple to default to summary conversations about fashions, prompts, and orchestration. However probably the most compelling tales I see are those the place AI unlocks one thing deeply human—creativity, instinct, and experience—at fully new velocity and scale.
That’s why I used to be excited to host Shade Meets Code: Pantone’s Agentic AI Journey on Azure, a webinar that includes two Pantone leaders, Kristijan Risteski, options architect, and Rohani Jotshi, senior director of engineering. Throughout the session, Kris and Rohani shared how they’re making use of agentic AI to one of the crucial foundational parts of artistic work: shade—and the way an AI-ready database, Azure Cosmos DB, performs a central position in making that potential.
The problem: Scaling shade experience in a real-time, interactive world
Pantone is well known as a world authority on shade. For many years, their groups have mixed human experience, shade science, and pattern forecasting to assist designers and types outline, talk, and management shade throughout industries—from trend and product design to packaging and digital experiences.
However as Pantone defined within the webinar, translating that depth of experience into a contemporary, conversational AI expertise got here with actual challenges. Creating shade palettes is each time consuming and demanding to the design course of. Designers usually collect inspiration by navigating between instruments, shade pickers, and pattern studies earlier than they ever land on a usable palette.
Pantone noticed a possibility to rethink that workflow fully: What if designers might work together with a long time of Pantone analysis, pattern information, and shade psychology by a chat-based interface—and generate curated palettes immediately?
Introducing the Palette Generator: An agentic AI expertise
The result’s Pantone’s Palette Generator, an AI-powered expertise launched at least viable product to collect actual consumer suggestions and iterate quickly. Relatively than providing static suggestions, the Palette Generator makes use of multiagent structure to reply dynamically to consumer intent, conversational context, and historic interactions.
Within the webinar, the Pantone crew described how they designed the system to incorporate specialised brokers—similar to a “chief shade scientist” agent and a palette technology agent—every accountable for completely different elements of reasoning, context retrieval, and response technology. These brokers work collectively to ship curated shade palettes that replicate Pantone’s proprietary information and experience.
What stood out to me was not simply the sophistication of the AI, however the architectural self-discipline behind it. Agentic AI isn’t nearly fashions—it’s additionally about information.
Why Azure Cosmos DB was foundational
On the coronary heart of Pantone’s Palette Generator is Azure Cosmos DB, serving because the system’s real-time information layer. Azure Cosmos DB is used to retailer and handle chat historical past, immediate information, message collections, and consumer interplay insights—all of that are important for responsive, quick, context-aware brokers.
As we did our analysis to search out the most effective persistence storage, we explored completely different databases. What we discovered for Azure Cosmos DB was how simple it was to combine it into our methods. We had been capable of make our preliminary proof of idea with a number of strains of code and retrieve all the information very, very quick, like in a number of milliseconds.
Kristijan Risteski
Azure Cosmos DB was additionally chosen due to its scale, permitting Pantone to serve customers all around the world with quick information retrieval.
This can be a essential level. As purposes shift from “doing” to “understanding,” databases should help way over easy transactions. They should deal with huge volumes of operational information, adapt as AI workflows evolve, and help superior eventualities like conversational reminiscence, analytics, and vector-based search.
Pantone’s structure demonstrates what it means to be “AI-ready.” Azure Cosmos DB supplies the scalability and adaptability wanted to trace consumer prompts and conversations throughout classes, together with analytics that assist Pantone perceive how prospects work together with the Palette Generator over time.
From textual content to vectors—and what comes subsequent
One other perception Pantone shared in the course of the webinar was how their structure is evolving to enhance relevance, accuracy, and contextual understanding. Whereas the present system already helps wealthy conversational experiences, the crew outlined subsequent steps that contain shifting from conventional textual content storage to vector-based workflows. This consists of embedding consumer prompts and contextual information, permitting for vector search, and enriching responses with deeper semantic understanding.
Azure Cosmos DB performs a job right here as nicely, supporting vectorized information, integrating with agent orchestration, and embedding fashions deployed by Microsoft Foundry. This permits Pantone to iterate with out rearchitecting the whole system—an important functionality when working in a fast-moving AI panorama.
Actual-world outcomes from agentic structure
Pantone didn’t simply speak about imaginative and prescient—they shared concrete outcomes from actual utilization of the Palette Generator. Based on the webinar information, customers throughout greater than 140 international locations engaged with the device, producing hundreds of distinctive chats throughout the first month of launch and interacting in dozens of languages. The system noticed a number of queries per consumer session, indicating that designers had been actively experimenting, refining prompts, and exploring concepts conversationally.
Simply as importantly, Pantone emphasised how quickly they’ve been capable of study and adapt. Immediate sensitivity, consumer habits, and architectural tradeoffs round velocity, value, and reliability all knowledgeable ongoing refinements. Azure Cosmos DB’s flexibility made it potential to seize these insights and evolve the expertise with out slowing innovation.
Classes for anybody constructing agentic AI
Pantone’s journey reinforces a number of classes I see repeated throughout prospects constructing AI brokers on Azure:
- Agentic AI is inherently information pushed. With no real-time, scalable database layer, even probably the most superior fashions wrestle to ship constant, context-aware experiences.
- Suggestions loops matter. By capturing prompts, responses, and consumer interactions in Azure Cosmos DB, Pantone can constantly enhance each the AI and the product expertise itself.
- Flexibility is nonnegotiable. AI architectures evolve rapidly—from orchestration patterns to embedding methods—and databases should evolve with them.
What Pantone has constructed with the Palette Generator is greater than a characteristic; it’s a blueprint for a way organizations can translate deep area experience into clever, agent-driven purposes. By combining Microsoft Foundry, Azure AI companies, and an AI-optimized database like Azure Cosmos DB, Pantone is displaying how creativity and know-how can transfer ahead collectively.
As extra organizations embrace agentic AI, the query received’t be whether or not they can deploy fashions—however whether or not their information foundations are able to help real-time understanding, reminiscence, and scale. Pantone’s journey makes that reply clear: AI-ready purposes begin with AI-ready information.