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Structured Intelligence for Complex Documents

SciFork Insight parses and indexes complex PDFs, reports, and technical documentation into a layout-aware knowledge system. Hierarchy, tables, multi-column flows, and spatial references are preserved, enabling precise, verifiable answers.

Built for Document Complexity

Designed to handle structured, multi-format documentation without losing layout or context.

01

Verifiable Answers

Every response is traceable to the exact document, page, and indexed segment from which it was derived. Citations are spatially bound to the source — ensuring auditability and review.

02

Layout-Aware Parsing

During ingestion, the system preserves document hierarchy, multi-column layouts, tables, and other structural elements. Retrieval is grounded in the document’s structure, not just extracted text, ensuring greater accuracy and contextual integrity.

03

Controlled Access

Role-based access controls define who can query the system, manage documents, allocate credits, and administer knowledge bases, enabling structured and accountable control across the organization.

Predictable Usage. Transparent Pricing.

Usage-based consumption with fixed monthly tiers.
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Scale with Control.

From individual analysts to institution-wide deployments.

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  • White-Label Deployment Deploy the layout-aware knowledge system under your own brand. Full interface and domain customization available.
  • Custom SLAs Enterprise-grade uptime guarantees and priority support.
  • API Integration Headless integration into internal systems and client platforms.

How It Works.

The complete pipeline from raw document ingestion to verifiable grounded answers.

  • 1. Layout-Aware ParsingDocuments are ingested using layout parsing to analyze their structural elements, including headings, paragraphs, lists, and tables. Instead of flattening content into plain text, the system identifies structural boundaries and logical reading order, preserving the organization of the original document.
  • 2. Structure-Based Chunking & IndexingParsed documents are segmented into context-aware sections based on layout and content structure. Each segment is indexed with metadata linking it to its original document and page location. This enables retrieval that respects document structure rather than relying solely on keyword matching.
  • 3. Semantic RetrievalWhen a query is submitted, the system performs semantic search across the indexed segments within the selected knowledge bases. Relevant sections are identified based on meaning and contextual alignment, ensuring that responses are grounded in the most relevant document portions.
  • 4. Grounded Answer GenerationResponses are generated strictly from retrieved document segments. Each answer includes references to the original document and page, allowing users to inspect the source material directly and verify the information.
  • 5. Access Control & Knowledge ScopeKnowledge bases are logically segmented per user or team. Access permissions define who can query documents, manage content, and administer knowledge environments, ensuring controlled deployment across organizations.

Early Users

Organizations exploring SciFork Insight

ActionNCD International

Geneva

Supporting access to global health guidelines and standards for healthcare teams.

Research Community

Academic & independent researchers

Exploring document-grounded AI for research and technical documentation.

SciFork Insight is designed for organizations that require precision, control, and verifiable document intelligence.

If you would like to discuss deployment options, we’re ready to assist.

Frequently Asked Questions

Everything you need to know about the product and how it works.

SciFork Insight is a platform that allows organizations to build AI-powered knowledge bases from document collections. Users can upload documents, ask questions, and receive answers grounded in the original sources.

The platform is designed to work with complex documents such as research papers, reports, policy guidelines, and technical documentation. These documents often contain structured layouts, tables, and figures.

When a user asks a question, the system retrieves relevant sections from the document collection and uses them to generate a response. The answer is grounded in the retrieved content.

Each response is tied to specific sections of the source documents. Users can inspect the referenced text directly, ensuring that answers remain traceable to their original context.

Yes. SciFork Insight supports team-based knowledge environments where managers can create knowledge bases, upload documents, and allow team members to query the information.

No. The platform is designed to be accessible to non-technical users. Creating a knowledge base typically involves uploading documents and then interacting with the assistant through natural language questions.

The platform is primarily designed for organizations that work with large document collections and require reliable, verifiable access to information.

Many tools allow users to chat with a single document. SciFork Insight focuses on structured knowledge environments built from entire document collections and emphasizes traceability, organization, and team workflows.

Yes. Every response includes references to the document sections used to generate the answer, allowing users to inspect the original material.

A knowledge base is a collection of documents organized within the platform. Once documents are uploaded, users can ask questions about the entire collection and retrieve information from across multiple sources.

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