Smarteeva Orchestra: No-Code AI Agent Builder for MedTech Quality & Regulatory Teams

TLDR

Smarteeva Orchestra is an AI agent orchestration platform built for MedTech quality and regulatory teams. It uses a drag-and-drop canvas where users assemble pre-built functional blocks, form readers, summarizers, risk classifiers, and complaint routers into working agents. Each agent can connect to multiple enterprise systems and run on multiple large language models (LLMs) simultaneously. Teams can go from a blank canvas to a production-ready agent in as little as ten minutes. Orchestra agents plug directly into Smarteeva’s existing modules for Complaint Handling, Adverse Event Reporting, Recall Management, Post-Market Surveillance Reports, Risk Management, and Registration Management, so the intelligence sits inside the workflow - not next to it.

Smarteeva Orchestra: Build AI Agents for Quality and Regulatory Teams - No Code Required

What is Smarteeva Orchestra?

Smarteeva Orchestra is an AI agent orchestration platform designed for regulated industries. It sits inside the Smarteeva product suite and gives quality, regulatory, and compliance teams the ability to create, test, and deploy AI agents without writing code.

The platform works on a drag-and-drop canvas. Users pick from a library of functional building blocks, each one performs a specific task, like reading a complaint form, generating a summary, classifying a risk level, or routing a record to the right queue. Connecting those blocks creates an agent: a sequence of steps that runs automatically when triggered.

What separates Orchestra from general-purpose AI agent frameworks is scope. It is not a horizontal tool adapted for healthcare. It was built from the ground up for the workflows MedTech quality and regulatory teams actually run: complaint intake, adverse event evaluation, reportability decisions, recall coordination, and post-market surveillance. The agents it produces operate inside those processes, not alongside them.

Orchestra also supports multi-LLM architectures. A single agent can route different tasks to different language models based on what performs best for that specific step: one model for summarization, another for classification, a third for structured extraction. This removes the dependency on any single AI vendor and lets teams optimize accuracy task by task.