TeamDynamix reduces IT workloads by up to 70% with Azure data and AI

“We landed on building atop AI capabilities in Azure because we felt that we could deliver value faster, cost-effectively, at scale, and more securely, and the data and privacy policies were the strongest for our customers,” says Andrew Graf, Chief Product Officer, TeamDynamix. “We also felt that working with Microsoft’s continual innovation would reduce our R&D burden (cost and speed) over the long term.”

Azure provided a scalable, enterprise-ready platform for TeamDynamix to design and manage AI agent–led workflows. These workflows handle high volumes of requests, tie directly into its no-code ITSM and ESM platform, and remain dependable as adoption increases. This approach has allowed the team to focus on delivering consistent outcomes for customers rather than managing underlying infrastructure, reducing operational overhead while enabling innovation.

It was a priority for TeamDynamix to ensure AI-driven interactions behaved predictably in real time. Requests had to be accurately interpreted, grounded in the client’s data. The team created AI agents that helped ensure quality results and enabled “human in the loop” (HITL) where appropriate while resolving routine issues autonomously.

“At every stage, we built on Azure. It gives us access to the most capable language models,” Graf says. Azure vector databases power retrieval-augmented generation (RAG), grounding AI responses in each customer’s own data, not generic prompts. Azure Machine Learning learns from each customer’s historical ticket data to classify, prioritize, and route with precision. Graf notes, “This would be a lot more challenging without the depth and reliability of the Microsoft AI ecosystem.”

Powering AI at scale with a unified Azure platform

Azure OpenAI in Foundry Models interprets requests and generates responses; without it, TeamDynamix would have had to build and maintain its own model layer. Azure AI Search grounds those responses in each customer’s data, helping avoid the generic outputs Graf flagged.

Azure Cosmos DB fuels vector search across customer datasets that, in some environments, scale to tens of millions of documents. “TeamDynamix chose Azure Cosmos DB because it was a great blend of affordability, speed of implementation, easy maintainability, and once we set it up, we do very little to maintain it,” says Fred Pandolfi, VP of Engineering – Process Automation, TeamDynamix. “We had great scalability at a cost-effective price. And we get to bring in large amounts of data. I think our average query times are about 150 milliseconds.” Azure Cosmos DB serves as the backbone for TeamDynamix’s AI-driven workflows, powering RAG-based search, surfacing related tickets and knowledge articles, and enabling the system to generate suggested solutions. Its flexibility also allows customers to build and reuse “knowledge sets” across multiple scenarios, supporting reliable, scalable retrieval of data to answer questions or automate resolutions.

Azure Kubernetes Service (AKS) then operationalizes this entire flow by running the agent workloads that connect reasoning to retrieval, so the team can deliver fast, reliable responses even as both data volume and user demand scale. It is the production runtime for TeamDynamix’s agents, providing the resilient platform that keeps performance consistent as both data and request volumes grow. “We use AKS heavily for many different microservices, but in terms of AI, it specifically allows us to easily target different compute levels, which is really helpful,” says Chris Neiger, VP of Engineering – Work Management, TeamDynamix.

“The Microsoft platform allows us to future-proof our investment, as well as the investment of our customers, by quickly taking advantage of emerging technology and models,” says Pandolfi. “This means that you can evolve at a pace that you wouldn’t be able to without the Microsoft platform.”

Driving measurable impact and expanding what’s possible with AI

By reducing routine demand and accelerating resolution, the platform improves accuracy and efficiency at scale. As a result, technicians can focus on higher-value tasks, improving the quality of resolution across customer interactions.

“Our customers can deflect between 30% and 60% of the tickets that come into the help desk by solving them through automation and AI, and then if a ticket does route to the service team, we help clients resolve that issue up to 90% faster. So, we make people more efficient with the use of technology,” says Rod Mathews, CEO, TeamDynamix.

Being able to redirect or resolve such a large percentage of tickets has led to a reduction in support workload by up to 70%, eliminating as much as two to three months of manual tasks per technician each year. With TeamDynamix, companies can accelerate resolution while preserving service quality and freeing teams to focus on higher-value work.

In addition to resolving tickets faster with AI and automation, IT teams also benefit from the automation of many manual tasks. According to a market study conducted by TeamDynamix with International Data Corporation (IDC), IT teams are spending an average of two to three months a year on repetitive “toil” such as password resets, identity and access updates, and onboarding and offboarding. When these tasks are automated, their time can be spent on strategic work or meaningful and complex IT problems.

“If you’re buried under a mountain of tickets, you don’t have time to go do all those things to really help people,” says Mathews. “And what this solution does is two things: it helps people have a lot more satisfaction in their careers and it drives better satisfaction from the employees that are using those services. Everybody benefits.”

With the solution in place, TeamDynamix can expand agent-led workflows across additional service scenarios, refine escalation paths, and equip technicians with richer context to resolve complex issues faster.​ Mathews says, “We’ve relied on Microsoft everywhere we can, as we deliver our solution, as we deploy AI, as we do natural language types of things. We’ve used Microsoft technology to deliver all of that.”

TeamDynamix shows that AI can be applied in production within SaaS platforms to handle routine demand while improving resolution speed and consistency. Grounding responses in each customer’s data makes this approach reliable at scale, enabling automation to resolve routine issues while maintaining accuracy and trust. The company runs on an Azure-based, enterprise‑ready AI app platform to design, manage, and scale powerful AI apps and agents more securely and cost-effectively. As a result, TeamDynamix has established a repeatable model it can extend across additional service scenarios as demand grows.

Discover more about TeamDynamix on LinkedIn.