From 28 days to under 5. Automating the R&D workflow that was slowing everything down.
- 80%
- cycle-time reduction
- 28 days
- to under 5
About the client
A pharmaceutical organization in active drug discovery and research. The core scientific work was proceeding well. The constraint was the operational infrastructure around it: the data collection, synthesis, and analysis researchers had to complete before acting on findings was slow, manual, and dependent on a few people with the context to run it correctly.
Challenge
A 28-day manual workflow, repeated every cycle, across a high-stakes research program.
The workflow at issue was the collection, consolidation, and analysis of R&D data from multiple sources feeding research decisions. It was executed by hand across multiple systems, with significant formatting, validation, and synthesis before outputs were usable. At 28 days per cycle, the cost was researcher time, the delay between data and action, and decisions made on information already weeks old. The manual process also introduced variability that added downstream rework.
What we built
An intelligent automation system designed for the specific architecture of this research environment. Assessment mapped every step, data source, decision point, and handoff. The system automated the collection, consolidation, and preliminary analysis, integrating with the client's existing data systems rather than replacing them. Intelligent agents handled the steps with variable inputs and conditional logic, where rule-based automation would be brittle. Human review was preserved where research judgment genuinely mattered. It was built for the auditability and data-integrity requirements of a pharmaceutical environment from the start: every automated step produces a traceable record, outputs are validated before delivery, and anomalies are flagged for human review rather than passed downstream.
Results
A 28-day process became a sub-5-day process, an 80% reduction in cycle time. Decisions that waited four weeks for supporting data now wait less than one. Experimental cycles can run at the rate the science determines, not the operational infrastructure. The manual effort the workflow consumed was reallocated to higher-value research.
- 80%
- cycle-time reduction
- 28 days
- to under 5
