Case Studies
Our teams couldn't easily access and share AI. Now anyone can build and share a tailored agent for their specific function — without engineering support.
As an insurance and benefits firm with many teams of varied needs, the firm needed a way to give every team member access to AI without requiring engineering support for each new use case. The goal was a self-service platform where anyone could build, share, and collaborate on conversational AI agents tailored to their specific function.
Building useful AI agents had been gated on technical resources. Business teams had ideas but no way to act on them independently.
Every new AI use case required development resources to implement. Business teams with clear needs waited in engineering queues while the backlog grew.
Different teams used different tools for language tasks — summarization, writing, research, support — with no shared platform, no knowledge reuse, and no visibility across functions.
When individuals built useful AI workflows, there was no structured way to share them with teammates, gather feedback, or iterate collaboratively.
An Azure OpenAI Agents platform built by Data Science Dojo on Ejento. This gives every team member a governed, self-service environment to create and share AI agents.
Business users create conversational agents tailored to specific tasks or job functions — content summarization, customer support, research, writing — without engineering involvement.
Agents can consume data from PDF documents, web links, YouTube videos, and plain text, giving them the context needed to provide accurate, relevant responses.
Agents are shareable across teams. Colleagues can comment, give feedback, and contribute to refinement. This turns individual tools into organizational assets.
The platform enforces industry-standard security and privacy controls. Data stays protected throughout agent creation, deployment, and operation.
Any team member can build and deploy an AI agent for their specific workflow — without waiting on engineering. AI capacity scaled with the organization.
Agents built by one team are discoverable and reusable by others. The platform created a collaborative layer that turned individual productivity gains into organizational ones.
Language tasks that previously consumed analyst and specialist time are now handled by agents, freeing teams to focus on strategic priorities.
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