What challenges did SciOne AI face before AI implementation?

R&D teams spend too much time on management, sample preparation, equipment management, and inventory tasks instead of core research work. A single-agent architecture became hard to scale because of intent ambiguity, module coupling, and parallel task scheduling complexity.

What was the impact of the AI solution?

Reduced product time to market by 50%. Reduced AI agent development time by 50%. Improved R&D productivity by 20% in intelligent recommendations for one customer scenario.

Source

SciOne AI Rebuilds Lab Workflows with Multi-Agent Architecture Using Amazon Bedrock

Use case typeWorkflow orchestrationUpdated Jun 13, 2026

SciOne AI is transforming R&D and lab operations through digitization and AI, delivering an AI-powered IDE for researchers in the chemical and life sciences industries. The company developed more than ten AI agents for lab operations, including equipment, inventory, PLM, recipe, sample, and test agents, to streamline repetitive research workflows. The platform uses a supervisor agent to route tasks to sub-agents, integrates customer-side tools for domain-specific scenarios, and builds a knowledge base for lab manuals, equipment documentation, and safety specifications.

Organization: SciOne AI
Industry: Pharma
Location: China
Published: May 2026

Reported outcomes

−50%

timeTime & speed

+20%productivity

Strategic outcomes

New product / capabilityBuilt multi-agent lab workflow platformNew product / capabilityCreated AI-powered IDE for researchersBetter decisions & insightAdded intelligent recommendation capabilitiesSpeed & agilityRebuilt workflows with scalable multi-agent architecture

Catalog median for time & speed deployments: −60% across 724 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 4

1Multi-Agent Orchestration
2R&D Automation
3Lab Operations

R&D teams spend too much time on management, sample preparation, equipment management, and inventory tasks instead of core research work.
A single-agent architecture became hard to scale because of intent ambiguity, module coupling, and parallel task scheduling complexity.

Implemented multi-agent collaboration on Amazon Bedrock with a supervisor agent and task-specific sub-agents.
Used AWS Lambda tools for authentication and timestamp conversion inside agent workflows.
Deployed modules on Amazon EKS and built a RAG knowledge base with Amazon Bedrock and Amazon OpenSearch Service for lab resources.

Technologies

Amazon Bedrock Agents Amazon OpenSearch Service Amazon EKS AWS Lambda Amazon EC2

Reduced product time to market by 50%.
Reduced AI agent development time by 50%.
Improved R&D productivity by 20% in intelligent recommendations for one customer scenario.

Architecture

SciOne AI uses Amazon Bedrock Agents multi-agent collaboration with a supervisor agent and multiple sub-agents, AWS Lambda tools for workflow functions such as authentication and timestamp conversion, Amazon EKS for containerized deployment, and Amazon OpenSearch Service as part of a RAG knowledge base.

Sources & evidence1

Groundedness: 4/5Type: Customer StoryPublished: May 27, 2026Publisher: AWSEvidence: PrimaryConfidence: High

Primary source

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Customer evidence