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Agentic AI
Ask your documents. Get cited answers.
Capabilities
Index documents from SharePoint, Google Drive, Confluence, Notion, local file shares, and databases. Support for PDFs, Word, Excel, PowerPoint, HTML, Markdown, and email archives.
Every answer includes citations linking to the specific document, page, and paragraph from which the information was derived. Users verify accuracy by clicking through to the source material.
Find information through natural language queries rather than keyword matching. Filter results by document type, department, date range, and access level. Understand synonyms and context in queries.
Respect existing document permissions from source systems. Users only see answers from documents they are authorized to access. No information leakage across permission boundaries.
Maintain context across multi-turn conversations. Ask follow-up questions, refine queries, and explore related topics without restating context. Conversation history is searchable and shareable.
Use Cases
According to McKinsey, knowledge workers spend 19% of their time searching for and gathering information — nearly one full day per week lost to finding answers that exist somewhere in the organization. The RAG Knowledge Assistant indexes company policies, process documents, training materials, and institutional knowledge into a searchable AI assistant that delivers instant, cited answers to employee questions. A 2024 Gartner Knowledge Management study found that organizations deploying RAG-based knowledge assistants reduce average information retrieval time from 8 minutes to 30 seconds and decrease repeat questions to HR and IT help desks by 45%. The system handles onboarding queries (benefits enrollment, leave policies, expense procedures), technical questions (system documentation, API references, troubleshooting guides), and process inquiries (approval workflows, compliance requirements, vendor management procedures). Every answer includes source citations, so employees can verify accuracy and explore related documentation. Knowledge gaps — questions the system cannot answer — are logged and routed to subject matter experts for content creation, continuously expanding the knowledge base.
The Thomson Reuters Legal Department Operations survey reports that corporate legal teams spend 35% of their time on document review and information retrieval rather than legal analysis and advisory work. The RAG assistant indexes contracts, regulatory filings, policy documents, legal opinions, and compliance guidelines, enabling lawyers and compliance officers to query the corpus in natural language. A 2025 Wolters Kluwer Legal Technology study found that AI-assisted legal research reduces research time by 65% while improving research comprehensiveness by identifying relevant precedents and clauses that manual search commonly misses. The system answers questions like 'What is our standard liability cap in vendor contracts?' or 'Which regulations apply to cross-border data transfers involving EU customers?' with specific citations to contract clauses and regulatory text. Version comparison features highlight changes between contract drafts, and compliance gap analysis identifies policy areas lacking documentation. Access controls ensure confidential legal matters are visible only to authorized personnel, with attorney-client privileged materials maintained in segregated indexes.
According to the Journal of the American Medical Association, clinical guidelines and treatment protocols are updated at a rate that makes it impossible for physicians to stay current through traditional continuing education — an estimated 7,287 new clinical trials are published monthly. The RAG assistant indexes clinical guidelines, hospital protocols, drug interaction databases, and research literature, providing physicians with instant access to current evidence during clinical decision-making. A 2024 BMJ Health Informatics study found that AI-assisted clinical knowledge access reduces diagnostic uncertainty by 28% and improves guideline adherence by 34% compared to manual reference lookup during patient encounters. The system answers queries like 'What is the first-line treatment for resistant hypertension in patients with CKD stage 3?' with cited references to current guidelines and supporting evidence. Drug interaction checking queries return comprehensive interaction profiles with severity ratings and management recommendations. Access controls ensure patient-specific clinical data remains within authorized clinical team access, and all queries are logged for quality assurance and audit purposes.
Frequently Asked Questions
The RAG architecture constrains the AI to answer only from retrieved documents rather than generating responses from its training data. Every claim in the response is grounded in a specific document passage, and source citations enable human verification. Confidence scoring flags responses where retrieved evidence is weak or ambiguous. If no relevant documents exist, the system states that it cannot find an answer rather than fabricating one.
The platform processes PDFs (including scanned), Microsoft Office (Word, Excel, PowerPoint), Google Workspace (Docs, Sheets, Slides), HTML, Markdown, plain text, email archives (EML, MSG, MBOX), Confluence pages, Notion exports, and database records. Scanned documents go through OCR before indexing. Audio and video files are transcribed and indexed for content search.
The platform inherits access controls from source systems — SharePoint, Google Drive, Confluence permissions are mapped to the RAG index. Users only see answers derived from documents they are authorized to access. When a user asks a question, the retrieval step filters documents by that user's permissions before generating the answer. Permission changes in source systems sync to the RAG index within minutes.
The system supports real-time, scheduled, and on-demand synchronization with source systems. Real-time sync captures document changes within minutes for critical sources. Scheduled sync runs hourly or daily for larger document repositories. Deleted documents are automatically removed from the index. Version tracking ensures the system always references the latest version of each document.
Yes. The platform indexes documents in 40+ languages and supports cross-language retrieval — you can ask a question in English and receive answers from Hindi or Tamil documents. Translation and cross-language semantic search operate at the embedding level, maintaining accuracy across language boundaries. This is particularly valuable for organizations with regional documentation in multiple Indian languages.
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