Sharly AI | Research Assistant for Summarizing, Comparing & Citing Documents

What

Sharly AI is an AI research assistant for summarizing, comparing, citing, and discussing documents in a shared workspace. It is positioned for individuals, teams, and organizations that work with dense or sensitive information, including researchers, analysts, product teams, compliance teams, universities, and think tanks.

The core workflow centers on uploading or connecting documents, asking questions across them, extracting structured insights, comparing key points, and verifying every answer against source material. The product appears to sit in the document intelligence and collaborative research category, with emphasis on source-backed outputs, team coordination, and controlled handling of private research.

Features

• Document summarization and highlights: Generates key insights, summaries, and highlights from documents quickly to reduce time spent reading dense material.
• Cross-document questioning and comparison: Lets users ask one question across multiple documents and compare metrics, assumptions, or findings in a single thread.
• Source-backed answers with citations: Attaches verifiable sources to responses and supports APA, MLA, and Chicago citation styles for more defensible research output.
• Collaborative workspace and Q&A: Provides shared workspaces, inline feedback, notes, and team question threads so groups can align on findings and next steps.
• Docs-only answer mode: Restricts answers to uploaded materials, which is useful when teams need bounded reasoning and tighter control over what informs outputs.
• Security and governance controls: Includes activity logs, role-based permissions, and encryption at rest and in transit to support accountability and private document handling.

Helpful Tips

• Validate the citation workflow in your real use case: If citation accuracy is critical, test a representative set of complex documents and edge cases before wider adoption.
• Define document governance early: Shared workspaces, permissions, and activity logs are most useful when teams establish clear rules for uploads, access, and review responsibilities.
• Use docs-only mode for sensitive analysis: For legal, compliance, or internal research contexts, bounded-answer settings can reduce ambiguity about where answers come from.
• Assess comparison quality on mixed-source research: The product is strongest where teams need synthesis across many files, so evaluate how well it handles conflicting claims and inconsistent document structures.
• Match the rollout to the user group: Individual researchers may focus on summarization and citation, while teams and organizations should evaluate collaboration, traceability, and permission controls more closely.

OpenClaw Skills

Within the OpenClaw ecosystem, Sharly AI could likely support skills that turn document-heavy work into structured, agent-assisted research workflows. Likely use cases include an agent that ingests a project folder, generates a summary brief, flags conflicting claims, creates a citation map, and routes unresolved questions to subject-matter reviewers. Another likely workflow is a research ops skill that monitors uploaded materials, tags documents by theme, and prepares comparison views for strategy, compliance, or literature review meetings.

For target professions such as analysts, researchers, and compliance teams, this combination could shift work from manual document review toward traceable, collaborative decision preparation. If OpenClaw agents were layered on top, teams could likely build workflows for evidence extraction, policy comparison, report drafting support, and review audit trails. The source page does not confirm native OpenClaw integration, so these should be treated as plausible orchestration patterns rather than built-in product capabilities.