Comparing semantic Retrieval-Augmented Generation (RAG) knowledge systems against traditional keyword-based enterprise search tools.
Executive Evaluation Verdict
Traditional enterprise search returns 50 blue links requiring employees to read through 20-page PDFs to find answers. Semantic RAG synthesizes exact verified answers with direct paragraph citations in seconds.
Traditional Enterprise Search (SharePoint / Elastic)
Keyword matching that returns hundreds of unranked documents, broken links, and outdated policy manuals.
Fortiv Hybrid Vector RAG Knowledge Layer
Semantic search that understands natural questions, synthesizes exact answers, and cites verified internal policy clauses.
Feature & Capability Matrix
| Evaluation Criteria | Traditional Approach | Fortiv AI Approach | Strategic Impact |
|---|---|---|---|
| Answer Delivery Format | Returns a list of 50 document links requiring manual reading and skimming. | Delivers a concise, synthesized 2-paragraph answer with exact source citations. | Saves 15 minutes per employee search |
| Search Intelligence | Exact keyword matching; fails if the employee uses a synonym or phrasing variant. | Dense semantic vector embeddings that understand intent, synonyms, and context. | Finds answers even with imperfect phrasing |
| Document Format Support | Struggles to search complex tables, embedded charts, and scanned images in PDFs. | Multi-modal parsing that indexes structured tables, diagrams, and scanned forms. | 100% knowledge indexing coverage |
| Access Governance & Permissions | Broad indexing risks exposing confidential HR and executive documents to all staff. | Strict role-based access control (RBAC) filtering results based on user identity. | Zero unauthorized data leaks |
Process Workflow Comparison
Evaluation FAQ
The language model is strictly constrained to synthesize answers only from retrieved enterprise text chunks, citing the exact document and page number for every claim.
We support Microsoft SharePoint, Google Drive, Confluence, Notion, Jira, Slack archives, Zendesk help centers, and internal SQL databases.
Role-based access control (RBAC) filters search embeddings based on the user's active Active Directory / SSO identity before generating answers.
Real-time webhooks and incremental synchronization re-index modified files automatically, ensuring answers reflect the latest policies.
No. All vector embeddings and model inferences execute in private single-tenant SOC 2 Type II environments with zero external model training.
Document connector setup, chunking optimization, and permission validation typically take 2 to 3 weeks.
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