Engineering Behind the Color: Software for a Global Paint Brand
February 13, 2026 | 4 Minute Read
When people think about a global paint brand, they often picture color cards, finishes, and products on shelves. What they don’t see is the sophisticated software engineering happening behind the scenes, like the technology that helps employees, sales teams, and customers get accurate answers faster while protecting sensitive data.
Our journey with a global paint brand started with a simple but urgent question: how do we safely put AI into the hands of employees?
The original driver wasn’t innovation for innovation’s sake. It was security. Leadership needed a way to prevent employees from using public AI tools in ways that could expose confidential data or unintentionally train external models. The challenge became finding a secure, enterprise‑ready path to generative AI adoption.
From Secure AI Access to Smarter Knowledge
Bill Curry, VP of Consulting, Improving
The first use case focused on giving employees a safe environment to ask questions using AI. By keeping everything inside a protected cloud environment, the solution ensured that sensitive information stayed private and was never reused to train external models.
From there, the vision expanded. Instead of searching the open internet, employees could query internal product data sheets, finding years of institutional knowledge in seconds. Early versions were intentionally lightweight, prioritizing speed, usability, and adoption over perfection.
At the time, best practices for enterprise RAG systems were still emerging. The solution evolved alongside the technology itself, adapting as new capabilities became available and lessons were learned in real time.
Microsoft: The Enterprise AI Foundation
Microsoft played a central role in making this evolution possible.
The solution was built on Azure AI from the start, leveraging Microsoft’s secure, enterprise‑grade approach to generative AI. Azure ensured data protection, compliance, and clear boundaries around model training, removing one of the biggest barriers to internal AI adoption.
On the backend, Azure AI services provided the building blocks for indexing, search, and model orchestration. As Microsoft’s AI ecosystem matured, new capabilities often arrived just as the need for them became clear, accelerating progress without forcing major architectural rewrites.
On the frontend, Power Platform Canvas Apps enabled rapid delivery of a simple, intuitive user experience. Instead of spending months designing a custom interface, the team focused on functionality: capturing a user’s question and returning a meaningful answer.
Today, services like Azure AI Foundry bring these pieces together, reducing complexity and making it easier to assemble robust AI solutions using proven, cloud‑native components. What once required heavy custom development is now driven by configuration, integration, and intelligent design.
Improving: Engineering the Difference
While Microsoft provided the foundation, Improving shaped how the solution actually worked in the real world. We approached the engagement as an evolving system, not a one‑time build. Early experimentation revealed that the main challenge was understanding messy, real‑world data.
Product data sheets stretched back nearly two decades. They referenced discontinued products, overlapping recommendations, and inconsistent terminology across multiple acquired companies. What initially appeared to be AI hallucinations were often symptoms of outdated or conflicting source material.
Improving leaned into this complexity rather than oversimplifying it.
Attempts to fully normalize the data into rigid structures quickly hit practical limits. Instead, Improving helped design a smarter RAG approach, filtering invalid documents, categorizing content by relevance, and crafting prompts that guided the model toward accurate, context‑aware responses.
One of the most impactful differentiators was focusing on how questions were asked. Sales teams often posed broad, ambiguous questions that naturally led to weak answers. Improving introduced a reasoning layer that refined and deepened user questions before querying the data, dramatically improving response quality.
Instead of chasing AI hype, we applied engineering judgment, understood human behavior, and designed systems that work the way people actually think.
Built for What’s Next
Today, the platform continues to grow, becoming more capable, accurate, and aligned with how the business operates. While the idea of a single, fully autonomous enterprise knowledge brain may still be years away, this global paint brand has already proven what’s possible when the right technology meets the right engineering mindset.
Behind every color, there’s more engineering than you might expect. Contact Improving to learn how our engineering‑led approach can help you turn complex knowledge into real business impact.
