Current Experiments

The Discovery Bridge enables researchers to access scholarly content directly through AI assistants while preserving subscription boundaries and institutional access controls. Leveraging Model Context Protocol (MCP), agentic AI assistants can search scholarly content by way of users’ natural language queries, providing seamless access to the full-text content directly within existing AI workflows. By integrating with existing entitlement systems, the Discovery Bridge not only extends the value of publisher content to existing users, but also unlocks new future revenue opportunities through consumption-based licensing and corporate content bundles.

With the Discovery Bridge, publishers can capture more value as AI assistants become essential research tools, ensuring their content remains discoverable and accessible through the channels researchers increasingly prefer.

Key Benefits

• Protect market position as AI assistants become research workbenches: Publishers who make content natively accessible in these environments maintain relevance and capture usage; those who don’t risk becoming invisible to the next generation of researchers.
• Unlock new revenue without risk: Corporate content bundles and consumption-based licensing create genuinely new markets beyond traditional academic subscriptions, from pharmaceutical companies licensing biomedical research, to aerospace firms subscribing to engineering collections.
• Respect existing business rules while expanding reach: Integration with your entitlement systems maintains paywall effectiveness and subscription boundaries. You control content availability and pricing so the Discovery Bridge extends rather than disrupts your business models.
• Enhanced discovery with protected access: Researchers discover and access your content where they’re already working—inside AI assistants—through intuitive natural language search that respects institutional authentication and subscription controls.
• Safeguard research integrity: Automatically exclude retracted articles, ensuring only current, validated content reaches researchers.
• Future-proof infrastructure investment: Core capabilities like authenticated semantic search, access management, and efficient content discovery will remain valuable regardless of which AI protocols dominate.
• Simplified implementation: Leverage Silverchair’s existing platform services for vector database, entitlement integration, authentication, analytics, and optimization without separate infrastructure investments.
• Publisher-led innovation: As with all Silverchair AI Lab products, the Discovery Bridge has been iteratively developed, tested, and informed by our client development partners as well as our AI team’s deep domain expertise.

Use Cases

Software Applications

• Researcher Productivity Enhancement: Researchers search and access their institution’s full scholarly content directly within AI assistants, maintaining workflow context while querying thousands of articles without switching between platforms
• Corporate Research Teams: Organizations license industry-specific content bundles (pharmaceuticals, aerospace, semiconductors) with AI-enhanced access, accelerating R&D through natural language queries synthesized from curated scholarly collections
• Institutional AI Integration: Libraries enable MCP access as an institutional service, positioning themselves as facilitators of modern research workflows while respecting subscription boundaries

AI Licensing & New Revenue Opportunities

• Consumption-Based Revenue Models: Participate in AI-native business models with per-token pricing, creating new revenue streams from platforms like Perplexity with Silverchair providing usage tracking and reporting infrastructure
• Corporate Content Bundles: Industry-specific content collections create new markets beyond academic subscriptions—pharmaceutical companies licensing biomedical research, aerospace firms accessing engineering collections—with aligned revenue sharing
• Training Data Licensing: Phase 3 will support controlled, auditable content crawling for AI model training with clear usage tracking and compensation
• Tiered Access Models: Experiment with differentiated pricing—basic web access, enhanced MCP access, premium corporate licensing—capturing value from different use cases without disrupting existing subscriptions

We’re currently working on pilots with several clients, with a planned launch date of early 2026—contact your Account Manager to learn more.

Submission volumes continue climbing while editorial resources remain constrained. The ScholarOne Editor AI Console gives editors immediate, contextualized insights into manuscript quality without adding another system to their workflow. Working directly within ScholarOne’s manuscript details page, editors access AI-powered analysis that understands their journal’s specific aims and scope—delivering tailored summaries of methodology, ethical considerations, and publication readiness that reflect what matters most to their editorial community.

Unlike third-party AI tools that apply generic criteria across all submissions, the AI Editor Assistant adapts to your journal’s unique standards. Editors maintain complete control over the prompts that drive analysis, with the ability to view, customize, and refine them based on their discipline’s evolving needs. This transparency ensures AI serves editorial judgment rather than replacing it, while creating opportunities for editorial teams to share effective prompting strategies across their portfolio.

Built to keep pace with rapid AI advancement, the tool leverages the latest foundational LLM capabilities while remaining grounded in the editorial workflow. Editors can quickly incorporate insights into reviewer invitations and other communications, streamlining the manuscript screening process without disrupting established practices. The result is a more efficient path from submission to decision—one that scales with volume while preserving the editorial rigor that defines scholarly publishing.

Key Benefits

• Editorial Control as Core Value: We designed the Editor AI Console around a fundamental belief: editors and publishers should understand and direct how AI supports their work. Complete prompt transparency and editability aren’t nice-to-have features—they’re essential to maintaining the integrity of editorial judgment in an AI-augmented workflow.
• Journal-Specific Intelligence: Generic AI assessments don’t serve the diversity of scholarly publishing. By ingesting each journal’s aims and scope before analyzing any manuscript, the Editor AI Console provides contextualized insights that reflect what each editorial community values. This approach acknowledges that publication readiness criteria vary significantly across disciplines and journal missions.
• Workflow Integration, Not Disruption: The most powerful AI tools are the ones editors actually use. We built the Editor AI Console directly into the ScholarOne interface, accessible from the manuscript details page alongside existing tools like Integrity Checks. This integration respects established workflows while adding efficiency where editors need it most—in the initial screening process that determines which submissions merit deeper review.
• Community-Driven Evolution: We see the Editor AI Console as a foundation for shared learning across editorial communities. As editors customize prompts for their journals and disciplines, they generate valuable insights about effective AI integration in peer review. Our vision includes creating spaces for editors to share these strategies, building collective expertise that raises the bar for AI-assisted editorial work across scholarly publishing.
• Focused Development Path: Rather than launching with an expansive feature set, we’re starting with core manuscript screening and refining it through pilot partnerships. This measured approach lets us learn what truly serves editorial efficiency before expanding functionality. It also differentiates our strategy from third-party tools racing to add features—we’re committed to getting the fundamentals right first.

Use Cases

For Editors: The ScholarOne Editor AI Console provides immediate, journal-specific manuscript analysis without leaving ScholarOne. You control the prompts, you control the criteria, and you maintain the editorial judgment that defines your journal’s standards.

For Publishers: As submission volumes grow across your portfolio, the ScholarOne Editor AI Console helps editorial teams screen manuscripts more efficiently while ensuring each journal’s unique standards guide the analysis. Complete prompt transparency means you understand exactly how AI supports your editorial process.

We’re currently working on pilots with several clients, with a planned launch date of early 2026—contact your Account Manager to learn more.

The scholarly publishing landscape is flooded with AI tools making bold promises, but implementation remains complicated and validation uncertain. Article Intelligence brings experimentation directly to your Silverchair Platform article page through an intuitive sidebar widget, giving publishers a practical path to test AI-powered features in real-world reading contexts before committing to full deployment.

Rather than asking you to evaluate AI capabilities in isolation, we’re building pilots that integrate naturally into the reader experience. Early features include AI-driven related content recommendations that transform discovery patterns and plain-language summaries that expand article accessibility—all accessible through a single, configurable interface on your platform. Publishers can activate tools in their staging environments to observe how readers interact with AI enhancements in authentic browsing scenarios, gathering the insights needed to make informed decisions about which capabilities deliver genuine value.

This approach reflects how the Silverchair AI Lab operates: iterate quickly, validate with real usage data, and scale what works. Article Intelligence removes the friction from AI adoption by embedding experimentation in your existing workflow rather than requiring separate evaluation processes. As we continue developing new capabilities, the sidebar becomes an evolving showcase of AI innovation—available when you’re ready to explore it, unobtrusive when you’re not.

Key Benefits

• Experimentation Without Implementation Burden: The gap between AI promise and AI reality often comes down to implementation complexity. Article Intelligence bridges this gap by providing a pre-built framework for testing AI features without requiring custom development work or separate evaluation environments. Publishers can activate pilots in staging, gather usage data, and decide what merits production deployment—all through a consistent interface that minimizes technical overhead.
• Context-Driven Validation: Generic AI demos rarely reflect how features perform with your content, your readers, and your site architecture. Article Intelligence enables testing in the environment that matters: your actual platform with your published research. This context-driven approach surfaces insights you can’t get from third-party tools or standalone prototypes, revealing how AI capabilities integrate with your existing discovery and engagement patterns.
• Reader-Centric AI Development: We’re developing Article Intelligence features based on clear reader needs: finding related content more effectively, understanding complex research more accessibly, and navigating scholarship more efficiently. Each pilot addresses specific friction points in the reader journey rather than adding AI for its own sake. The sidebar widget keeps these enhancements available without dominating the article experience, respecting that research content remains the primary focus.
• Velocity as Competitive Advantage: AI capabilities are evolving at unprecedented speed, and scholarly publishers can’t afford slow adoption cycles. Article Intelligence gives the Silverchair AI Lab the flexibility to launch new features rapidly, test them with willing partners, and iterate based on real feedback. This velocity ensures our platform evolves with AI advancement rather than falling behind it, while publishers maintain control over which innovations they adopt and when.
• Progressive Enhancement Philosophy: Article Intelligence embodies our approach to AI integration: add capabilities that enhance the platform experience without requiring wholesale changes to publisher workflows or reader expectations. The sidebar exists as an optional layer that publishers can configure based on their strategic priorities. Some may prioritize discovery tools, others accessibility features, still others might experiment broadly. The framework accommodates all these paths while maintaining a consistent, manageable interface.

This feature will be ready for test in early 2026—contact your Account Manager to learn more.

Citation integrity matters deeply to research quality, yet manual verification remains time-consuming and inconsistent. Citation Check embeds AI-powered validation directly into the ScholarOne submission workflow, automatically extracting DOI-based references and validating them against authoritative registries like Crossref. Editors gain immediate visibility into problematic citations—mismatches between manuscript text and registry metadata, fabricated DOIs, malformed references—without leaving the manuscript review interface.

The system handles the complexity of citation extraction across different manuscript formats and citation styles, normalizing references into a consistent schema while flagging issues with clear severity indicators. Rather than simply reporting problems, Citation Check provides the canonical metadata and suggested corrections that editors and authors need to resolve issues efficiently. This transforms citation verification from a manual bottleneck into an automated quality gate that strengthens research integrity at scale. The result is faster manuscript processing, reduced rework cycles, and stronger citation accuracy standards across your publishing program.

Key Benefits

• Workflow-Embedded Integrity: Citation integrity tools often exist as separate systems, requiring manual file uploads and disconnected verification processes. Citation Check embeds validation directly in ScholarOne’s submission workflow, making citation verification an automatic step rather than an added task. This integration ensures every manuscript receives consistent scrutiny without requiring editors to change their established review patterns.
• Authoritative Validation, Not Guesswork: The system validates citations against Crossref and other authoritative registries rather than relying solely on pattern matching or formatting checks. This approach distinguishes between legitimate references with minor formatting issues and fabricated DOIs that appear well-formed but don’t exist. Editors receive clear indicators of what’s verifiable versus what requires investigation.
• Actionable Intelligence for Faster Resolution: Identifying citation problems matters less than resolving them efficiently. Citation Check provides suggested corrections based on canonical registry metadata, enabling editors to communicate specific fixes to authors. This precision reduces revision cycles and accelerates manuscripts through the review process.
• Scale Without Compromise: Manual citation checking doesn’t scale with rising submission volumes, forcing publishers to choose between thorough verification and timely processing. Citation Check’s intelligent caching and batch processing capabilities handle large manuscript volumes while respecting API rate limits, ensuring consistent integrity standards regardless of submission patterns.
• Early Detection, Reduced Rework: Citation problems discovered late in production create costly correction cycles and publication delays. Citation Check shifts validation to the manuscript review stage, catching issues when they’re easiest to address and preventing them from cascading into production workflows. This upstream approach protects both editorial efficiency and publication timelines.