Papercurve

Papercurve is a content review and approval platform designed for life sciences teams, covering the full content lifecycle from creation through to distribution. The platform targets growing life sciences organisations seeking to reduce compliance risks, eliminate manual processes, and accelerate the time it takes to bring reviewed and approved content to market. Papercurve integrates with Veeva Vault and incorporates generative AI capabilities to support faster, more accurate review workflows.

The platform is built around ease of adoption, with a focus on intuitive design that minimises the need for third-party IT resources during setup. Users include roles such as Associate Scientific Directors, Business Unit Directors, and Health Account Directors at life sciences and healthcare organisations.

Core Products

• Medical Legal Regulatory (MLR) Review: Reduces content review and approval times by up to 60% by consolidating comments, linking references, claims, and translations, and keeping all stakeholders aligned throughout the review process.
• Sales Enablement: Measures content usage and engagement to quantify marketing return on investment. Provides actionable data on who is viewing and downloading content to improve follow-up responses. Ensures sales and MSL teams have access to the most recent approved content versions, reducing compliance risk.
• Contract Lifecycle Management: Monitors contract expiry dates, warranties, and other key events to help increase revenue. Provides a single, controlled-access repository for contracts and supports error reduction throughout the contract authoring and review process.

Key Features and Capabilities

• Compare Versions: Highlights differences between two versions of a document, helping teams identify unauthorised changes or edits and reduce errors in the review process.
• Paige AI for References: An AI-powered referencing tool that makes the referencing process up to ten times faster. Paige AI recognises well-referenced claims, understands the intent of a claim, and suggests potentially relevant references based on previous usage, reducing the time spent manually linking references.
• Comments: Allows users to insert comments on specific text excerpts or on content generally. Comments can be searched, sorted, and filtered, and team members can be mentioned directly to increase response times.
• Approvals: Streamlines the approval workflow by enabling selected team members to review, record feedback, and formally approve content before release, supporting both productivity and compliance requirements.
• Share Link: Enables users to share content via a unique link that does not require a login or password to view. Share Link analytics track all engagement, including views and downloads.
• Real-Time Collaboration: Provides a single location for tracking review statuses and consolidating stakeholder feedback throughout the content lifecycle.

Integrations and Deployment

• Integrates with Veeva Vault, with a dedicated comparison available for teams evaluating Papercurve against Veeva Vault.
• Offers fast setup with immediate return on investment and no requirement for third-party IT resources.
• Includes a Partner Program for organisations seeking to work with Papercurve in a collaborative capacity.
• Provides resources including case studies, testimonials, data security documentation, a knowledge base, and downloadable materials.

Notable Customers and Testimonials

• Fresenius Kabi — Kim Jensen, Business Unit Director, noted the team's responsiveness and commitment to platform improvement.
• IMC — Kristel Bermejo, Associate Scientific Director, described Papercurve as a next-generation referencing and reviewing platform.
• NFA Health — Tamara Carvalho, Health Account Director, cited it as one of the most reliable tools used across client accounts.

Papercurve is headquartered in Toronto, Ontario, Canada, and can be contacted at [email protected]. The platform is positioned as an all-in-one solution for life sciences content compliance, review, and distribution workflows.