The Challenge
Scientific research is often bottlenecked by the sheer volume of literature and the manual labor involved in designing and documenting experiments. Researchers spend up to 40% of their time on administrative tasks and literature review, slowing down the pace of discovery.
We aimed to build a "Research Co-pilot" that could handle the cognitive drudgery, freeing up scientists to focus on high-level hypothesis generation and analysis.
The Solution: HEO (Hypothesis, Experiment, Observation)
HEO is an agentic workflow system designed specifically for bio-tech and material science labs. It acts as a force multiplier for research teams.
Workflow:
- Hypothesis Generation: HEO scans millions of papers in PubMed and ArXiv to identify gaps in current knowledge and suggest novel hypotheses.
- Experiment Design: It proposes detailed experimental protocols, including reagent lists and equipment settings, optimized for the lab's specific inventory.
- Data Analysis & Documentation: HEO automatically ingests raw data from lab equipment, formats it, and drafts the initial results section of the paper.
Implementation
HEO was integrated into the ELN (Electronic Lab Notebook) software of a mid-sized biotech startup. It used a RAG (Retrieval-Augmented Generation) architecture to ensure all suggestions were grounded in peer-reviewed science.
Results & Impact
The pilot team reported a 3x reduction in time spent on literature review and a 50% decrease in experimental errors due to miscalculated reagent ratios.
Most notably, HEO suggested a novel combination of compounds for a drug delivery mechanism that the human team had overlooked, which is now proceeding to pre-clinical trials.