AI Agents for Life Sciences

Transform Scientific Data into Trusted, Compliant Content

Narrativa’s AI Agents are designed specifically for Life Sciences organizations, enabling teams to turn complex scientific and clinical data into accurate, compliant, and high-quality content at scale.

From clinical documentation to medical communications, our platform supports the full content lifecycle—combining automation with domain-specific intelligence and control.

Our AI Agents are not generic tools. They are specialized systems trained and configured to operate within Life Sciences environments, where precision, traceability, and compliance are critical.

SPOTLIGHT

Discover how a Top 3 Pharma Company implemented AI Agents achieving:

  • 50% productivity gains
  • 76% time savings
  • 43% cost savings
  • 100% data accuracy

Core AI Agents

SDTM & ADaM

Datasets Builder

Automates the creation of clinical datasets (SDTM and ADaM), ensuring they are comprehensive, compliance-validated, and ready for analysis.

Tables, Listing, and Figures

TLF Generation

Generates Tables, Listings, and Figures (TLF) for clinical trials, streamlining the reporting process.

CSR

Table to Text

Converts complex tables into clear, narrative prose text, facilitating easier interpretation of data.

Patient Narratives

Narratives Inspector

Helps Medical Writers to QC and validate the automatically generated patient narratives.

Information Retrieval

Semantic Search

Enhances search capabilities by understanding the context and semantics of queries, improving information retrieval.

Document Generation

Document Planner

Organizes and plans the creation of regulatory documents, ensuring timely and accurate submissions.

Quality Control

QC Validator

Ensures the quality and consistency of documents through automated quality control checks.

Compliance

Compliance Specialist

Monitors and ensures adherence to regulatory compliance across processes and documentation.

Protocol Specific Agents

Protocol

Protocol Design

Assists in drafting clinical trial protocols, ensuring they meet regulatory standards and scientific rigor.

Protocol

Protocol Burden

Analyzes the burden of clinical trial protocols on patients and sites, optimizing trial design for efficiency.

Protocol

Protocol Study Population

Evaluates study populations to ensure diversity and representativeness in clinical trials.

Protocol

Protocol Auditor

Reviews protocols to ensure adherence to regulatory requirements and identifies any compliance gaps, promoting alignment with industry standards.

More about AI Agents

AI agents in life sciences streamline data management by automating the collection, cleaning, and structuring of data from multiple sources. This reduces manual handling time, freeing teams to focus on analytical tasks. Their integration capabilities ensure smooth data flow across platforms, crucial for maintaining a unified data strategy and preventing data silos. This interoperability supports informed decision-making and enables a comprehensive view of data assets. Additionally, AI agents foster interdisciplinary collaboration, bridging gaps between fields like biology and data science, which enhances drug discovery and development and accelerates solutions for complex health issues.

Data quality assurance is a critical aspect of AI agents in pharma. These systems continuously monitor data to validate outputs, ensuring all processes adhere to predefined standards. By detecting inconsistencies and flagging potential errors early, AI agents significantly reduce the risk of inaccuracies, enhancing the overall quality of data. This capability is essential for maintaining high standards in pharmaceutical data management, where precision is paramount.

This proactive approach allows organizations to address issues as they arise, rather than after the fact. For instance, if an anomaly is detected in a clinical trial dataset, AI agents can immediately alert the relevant personnel, allowing for swift corrective actions. This real-time capability is invaluable in maintaining the integrity of critical data and ensuring compliance with regulatory requirements.

1. Clinical decision support: By analyzing genetic data, agentic AI helps healthcare professionals identify disease risks and biomarkers, enabling earlier interventions and more informed treatment decisions.

2. Patient privacy & data generation: It addresses data scarcity and privacy constraints by generating synthetic datasets that preserve anonymity while still providing valuable insights for research and model training.

3. Administrative efficiency: Agentic AI automates time-consuming tasks such as insurance processing, documentation, and patient workflows, reducing the administrative burden on clinicians and allowing more focus on patient care.

4. Drug discovery & development: It accelerates research by analyzing large datasets to identify potential therapies, improving success rates while reducing time and costs.

5. Personalized medicine: By leveraging genetic information, agentic AI enables tailored treatments, optimizing effectiveness, minimizing side effects, and improving patient outcomes.

6. Medical imaging: It enhances diagnostic capabilities through image analysis and simulation, supporting disease detection, treatment planning, and medical training.