How Emergence AI cut research time by 70% with multi-agent AI
Pharmaceutical researchers were burning weeks on manual data gathering, coordination, and review handoffs. We built the AI system that gave them that time back.
Client
Emergence AI
Impact
70% faster research cycles
Timeline
6 months to production
Domain
Pharma / Life sciences
The problem: researchers stuck doing busywork instead of science
PBPK modeling predicts how drugs behave in the body. It is critical for drug development, dosage decisions, and regulatory approval. But the traditional workflow was painfully manual.
Researchers spent their days gathering API characteristics, creating molecular representations, coordinating data across disconnected systems, and managing multi-step analyses by hand. The bottleneck was not computational power. It was the hours lost to repetitive coordination that could have been spent on actual scientific decisions.
Emergence AI needed to solve this. Without automation, they faced slow iteration cycles, duplicated effort across teams, and a product story that would not hold up with serious pharmaceutical customers who demand rigor and speed.
At a glance
Client
Emergence AI
Impact
70% faster research cycles
Timeline
6 months to production
Domain
Pharma / Life sciences
What we built: AI agents that do the legwork
We delivered a multi-agent AI system that automates the end-to-end PBPK modeling workflow. Specialized agents handle distinct stages of the pipeline while an orchestration layer keeps state, handoffs, and outputs consistent across the board.
The key design principle: humans stay in control. Agents propose steps, assemble context, and prepare artifacts for expert review. They do not bypass governance. In pharmaceutical research, explainability and auditability matter as much as speed.
How we worked
Started from the real workflow
We mapped how PBPK work actually moves from inputs through modeling to review. Automation that fights the real process is automation that gets ignored.
Iterated with domain experts
Agent boundaries, prompts, and handoff points were refined based on how the research team actually needed the system to behave, not how it looked on a whiteboard.
Engineered for reliability
Predictable behavior, clear failure modes, and interfaces that make it obvious what each agent did and what needs human attention next. No black boxes.
Built to ship, not to demo
The system fits Emergence AI's product architecture so it evolves with their roadmap. This was not a proof of concept. It runs in production today.
Results
Tech stack
Building AI workflows in a complex domain?
We help teams go from concept to a product their users trust. No demos that die on the shelf. Production systems that work.
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