Enterprise RAG Knowledge Platform
A secure knowledge assistant that helps employees find reliable answers across company documents, policies, and operational resources.
About the Project
CiteMesh AI is an enterprise knowledge platform designed to make internal information easier to find and use.
Employees can ask questions in natural language and receive concise answers grounded in approved company documents. Every response includes source citations so users can review the original material before acting on the information.
The Problem & Objectives
Important organizational knowledge is frequently distributed across policies, manuals, reports, onboarding documents, and shared folders. Traditional keyword search can return many files without identifying the exact information an employee needs.
The platform needed to deliver direct answers while preserving source traceability, document permissions, and administrative oversight.
Identified Challenges
- [1]
Fragmented Knowledge
Critical business info distributed across multiple document formats (PDF, Docx) and scattered storage drives.
- [2]
Unreliable AI Responses
Standard foundation models hallucinating answers instead of confining responses to internal sources.
- [3]
Document Permissions Sync
Enforcing role-based document access rules at the vector retrieval level so employees only query authorized files.
- [4]
Source Verification
Providing dynamic source citations with document name, page, and exact paragraph matching for auditability.
How the Solution Was Designed
CiteMesh processes approved documents, divides them into searchable sections, and stores their embeddings in a vector database.
When a user asks a question, the system retrieves the most relevant authorized content and provides it to the language model. The response is generated using that context and includes citations linked to the original sources.
If reliable information cannot be found, the platform states that it does not have sufficient evidence instead of producing an unsupported answer.
Key Features Built into CiteMesh
Core knowledge retrieval features engineered to support secure document lookup and source-grounded answering.
Natural-Language Search
Employees ask complex, natural questions instead of hacking exact keyword queries.
Source-Backed Responses
Generates cited answers linked directly to original PDFs, highlighting the exact reference text.
Permission-Aware Retrieval
Applies ACL (Access Control List) filters during vector database queries to respect organization permissions.
Document Ingestion Pipeline
Processes file formats, parses text sections, and updates vector indexes automatically.
Feedback & Reinforcement
Allows users to rate answers, flagging inaccurate or outdated documents for administrative review.
Search Audit History
Logs questions, retrieved context passages, and generated answers for system monitoring.
Grounding Enforcement
Forces the model to reply 'unsupported query' if the local document base lacks sufficient evidence.
Smart Chunking
Optimizes document chunking strategy by sliding window layouts to retain context boundaries.
Retrieval Pipeline
The path from a document to a cited answer.
Ingest & chunk
Approved documents are parsed and divided into searchable sections.
Embed
Sections are converted into vector embeddings and indexed.
Permission-filtered retrieval
A query only surfaces sections the requesting user is authorized to see.
Generate with citations
The model answers using retrieved context, with citations linked to source documents.
Controlled fallback
If authorized evidence is insufficient, the system says so instead of guessing.
Technology Infrastructure
The frameworks and database components driving the CiteMesh platform.
AI & Pipeline
Backend API
Vector Database
Frontend Experience
Infrastructure
Need a secure, cited RAG search platform built for your teams?
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