Custom Azure AI Agent Enhances Accuracy and Performance

A manufacturer sought to explore how generative AI could improve operational efficiency and support workflows. With complex product documentation, service materials, and internal knowledge distributed across systems, the organization recognized an opportunity to apply AI to surface accurate, contextual answers more quickly for internal teams and external partners.

Leadership wanted a structured, measurable pilot grounded in Microsoft technologies that could demonstrate real-world value before scaling. The organization had access to Microsoft funding for AI experimentation, but needed a partner capable of designing the architecture, building the proof of concept, and ensuring the solution aligned with enterprise governance standards.

They engaged Emergent Software to lead a focused AI workshop and pilot engagement centered on building a production-ready AI agent using Azure AI Foundry. The objective was not simply to experiment with large language models, but to determine whether an AI-powered solution could reliably answer complex, product-specific questions with a high degree of accuracy.

Accuracy was critical. The manufacturer operates in a highly technical environment where incorrect responses could create downstream operational issues. A measurable performance threshold needed to be established before broader rollout. Early internal testing showed accuracy rates hovering around 81%. The engagement would require architectural refinement, evaluation rigor, and iterative tuning to meet enterprise-grade expectations. 

This was the first step in defining how AI would responsibly integrate into core support workflows across the organization.

Emergent Software structured the engagement as a phased Azure AI Foundry implementation, beginning with a collaborative AI workshop and progressing into a production-oriented proof of concept.

The team designed and deployed an AI agent architecture within Azure AI Foundry, leveraging Microsoft’s AI services to enable retrieval-augmented generation (RAG) against approved documentation sources. Rather than allowing the model to respond generically, the architecture constrained responses to validated internal content, ensuring traceability and governance.

To improve answer reliability, Emergent implemented a structured test harness capable of evaluating outputs against predefined question-and-answer sets. This grading framework allowed the team to quantify accuracy improvements over time and identify patterns in model failure modes. By introducing systematic evaluation instead of subjective review, the company gained a measurable way to determine readiness for production.

Through weekly architectural working sessions, Emergent collaborated directly with internal stakeholders to refine prompt structures, optimize retrieval logic, and improve document chunking strategies. Adjustments were made to how content was indexed and retrieved, ensuring that the model surfaced the most contextually relevant material before generating a response.

Over successive iterations, the AI agent’s performance improved from approximately 81% to 92% accuracy. This increase was achieved not through model retraining, but through architectural refinement, retrieval optimization, and disciplined evaluation processes within Azure AI Foundry.

In parallel, Emergent focused on enablement. The engagement was designed to ensure internal teams could understand and extend the architecture independently. By pairing engineering efforts with documentation and collaborative build sessions, the client gained not only a working AI agent, but also a deeper understanding of Azure AI best practices, governance considerations, and scalable deployment patterns.

The pilot was supported through Microsoft funding, which allowed the organization to explore the technology in a structured and cost-controlled manner while maintaining enterprise rigor.

This phased approach provided both technical validation and organizational confidence, laying the groundwork for expanded deployment across additional workflows.

The engagement delivered more than a proof of concept. It established a measurable, repeatable framework for AI evaluation and deployment within the organization’s Microsoft ecosystem.

By improving agent accuracy from 81% to 92%, the team demonstrated that Azure AI Foundry could support high-quality responses within a controlled enterprise context. The introduction of a grading harness and structured validation process ensured that performance improvements were data-driven rather than anecdotal.

Equally important, the organization gained a scalable architectural blueprint for future AI initiatives. The solution was built within Azure, aligned with existing governance models, and designed to integrate into broader Microsoft infrastructure. This eliminated the need for disconnected AI experimentation and positioned future deployments within a secure, compliant framework.

The pilot also strengthened collaboration between business stakeholders and technical teams. Subject matter experts were able to validate AI outputs against real-world scenarios, ensuring the solution reflected operational realities rather than theoretical use cases.

With a validated architecture, improved accuracy, and internal enablement in place, the organization is now positioned to expand AI capabilities thoughtfully and responsibly. The phased rollout approach ensures that additional workflows can be integrated using the same evaluation discipline and architectural standards established during the pilot.

What began as a focused AI workshop evolved into a foundational step in defining enterprise AI strategy. By pairing structured experimentation with measurable outcomes, Emergent Software helped this manufacturer move beyond AI exploration and toward scalable, production-ready innovation within the Microsoft ecosystem.