R&D teams and clinicians deal with massive amounts of biomedical data everyday - from electronic health records and clinical trial datasets to high-throughput omics and imaging studies. Turning this fragmented, multi-modal information into actionable insights, for example, for tracking disease progression, evaluating safety and efficacy, understanding PK/PD relationships, or designing clinical trials, or designing trials often requires high technical expertise and the massive overhead of manual data cleaning and analysis.

Polly Co-Scientist changes that. Through AI-assisted querying with live metadata enrichment, it enables scientists and clinicians to analyze complex multi-modal clinical data using simple natural language. Whether you point it at a new therapeutic area, a different proprietary database, or a fresh set of clinical trials, Co-Scientist instantly adapts and allows you to pull queries, analyse, and visualize entirely new domains with the same natural language ease.

The platform does the heavy lifting of writing the complex database SQL and Cypher queries behind the scenes so researchers can focus on the scientific reasoning. This means you can ask whatever you want or need whether you are exploring data schemas, designing clinical trials, uncovering predictive biomarkers, or conducting hypothesis testing and statistical insights, Co-Scientist pulls out precise insights without writing a single line of code or worrying about exact technical definitions.

Polly Co-Scientist: AI-powered Research Assistant

Polly Co-Scientist is an AI-powered interactive analytics and visualization engine - a primary interface for exploratory biomedical research. Connected to structured Atlas environments and Knowledge Graphs (we can do internal linking here), it bridges high-level scientific hypotheses with deep technical data execution. It is a collaborative engine that automates the extraction of high-resolution information ranging from molecular mechanisms and patient subgroups to efficacy outcomes and patient recruitment status into a structured, unified format.

Clinicians and researchers do not have to build technical pipelines; they simply ask a question, and the engine translates that scientific intent into a structured, visualizations without needing specialized coding or data engineering support.

Core Capabilities: Interactive Analytics & Visualization

Co-Scientist handles the heavy lifting of multi-source data integration and complex analysis and reasoning, so your team can focus on scientific insight:

• Conversational Data Retrieval: Query datasets in plain English to extract insights, trace multi-hop relationships, or analyze trial endpoints.
• Statistical Analysis: Run tests such as Fisher’s exact test, t-tests, or custom analyses to identify significant biomarker–outcome relationships and more.
• Dynamic Visual Outputs: Generate interactive graphs, exposure-response models, or complex Entity Relationship Diagrams (ERD) to visualize disease progression, drug responses, recruitment status and more.
• Strategic Dashboards: Build cross-indication dashboards evaluating metrics such as cost, time, probability of regulatory success, and ROI index for rapid evidence-based decision-making.
• Predictive Design & Simulation: Draft early-phase study designs by highlighting key considerations around dose, endpoints, timing, and patient stratification based on historical evidence.
• Adverse Event & Risk Prediction: Analyze recurring safety patterns across multiple studies to identify serious signals essential for developing novel therapies.
• Transparency: Every output includes a detailed view of the internal logic, execution plan, and exact Cypher/SQL queries for full verifiability.

From Raw Evidence to Actionable Insight

To ensure absolute precision, Co-Scientist follows a structured pipeline that turns fragmented documents into a dynamic analytics layer:

1. Schema Identification & Extraction: Automatically identifies and captures critical data points such as disease progression and recruitment status from diverse publications and internal reports.
2. Expert Validation: Extracted data exists in a structured format where experts can update, refine, or delete labels to ensure the highest data integrity.
3. Interactive Exploration: Researchers use the chat interface to explore early signs of efficacy, including biomarkers, imaging readouts, and emerging digital measures.
4. Strategic Strategy Generation: The engine evaluates market potential and overall development risk, drafting early-phase trial designs with estimated costs and timelines.

Benchmarked Reliability

Making high-stakes R&D decisions demands precision. Polly Co-Scientist has been rigorously evaluated-

Broad Applications Across the R&D Lifecycle

Co-Scientist powers the most critical phases of research and development by bridging multi-modal data:

• Cross-Therapeutic Insights: Connect mechanistic evidence with historical outcomes, surface predictive biomarkers, and evaluate small-molecule precedents across indications.
• Evidence-Driven Trial Design: Draft early-phase trial plans with estimated cost, timelines, regulatory probability, and translational feasibility, market potential and risk assessment across indications.
• Safety & Efficacy Modeling: Identify recurring safety patterns, trace early efficacy signals via imaging or digital measures, and assess clinical trial feasibility.
• Target & Indication Prioritization: Compare diseases or syndromes for market potential, clinical risk, and translational feasibility.
• Cohort & Metadata Retrieval: Quickly isolate specific populations, such as "non-responders in the BeatAML trial with RAS/MAPK pathway activation."
• Seamless Integration: Trigger an automated analysis by tagging the AI in Slack or Microsoft Teams, then use the "Deep Research" bridge to transition into the full analytics environment.

By moving the workflow from simple chat to an autonomous analytics and visualization engine, Polly Co-Scientist ensures R&D teams moves from discovery to insight faster than ever before.