Cloud-Dog RAG Agent

Executive Overview

Cloud-Dog RAG Agent is a secure, governed platform for retrieval-augmented generation across confidential, regulated and business-critical data. Built on the RAGFlow engine, it enables teams to index, search, analyse and automate work over documents, emails, wikis, tickets, databases and code — returning grounded, cited answers that meet compliance and assurance expectations. The platform orchestrates multi-agent workflows that combine retrieval, reasoning, tool use and structured actions.

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Summary

Cloud-Dog RAG Agent delivers secure, governed retrieval-augmented generation across confidential enterprise data. Teams can index, search, analyse and automate work over documents, emails, databases and code, receiving grounded, cited answers. Multi-agent orchestration, hybrid search, compliance controls and desktop integration accelerate knowledge-driven productivity across regulated organisations.

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Features and Benefits

Feature	Benefit
Confidential search across documents and databases	Protect sensitive data with governed access controls
Privileged-aware responses with source citations	Ensure compliance with audit-ready lineage and retention
Automated drafting for policies, briefs and reports	Accelerate privileged requests with grounded answers
Secure normalisation, classification and de-duplication	Reduce manual research through automated evidence gathering
Integrated messaging via Slack, Teams and email	Shorten policy and report drafting cycles significantly
Targeted research for markets, risks and compliance	Improve decisions with traceable verifiable source references
Office and Google Workspace desktop integration	Boost productivity across messaging and document tools
No-code workflows for approvals and publishing	Unify fragmented knowledge across the enterprise
Audit-ready lineage, redaction and retention enforcement	Integrate seamlessly with existing enterprise tools
API-first platform for systems and data pipelines	Demonstrate regulatory assurance with consistent controls

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Product Overview

Cloud-Dog RAG Agent enables teams to index, search, analyse and automate work over documents, emails, wikis, tickets, databases and code — returning grounded, cited answers that meet compliance and assurance expectations. The platform orchestrates multi-agent workflows that combine retrieval, reasoning, tool use and structured actions. Users can design flows visually or via APIs, enforce policy guardrails and publish reliable outputs into business channels such as Teams, Slack, email, ticketing and productivity suites.

Cloud-Dog unifies fragmented knowledge by connecting filesystems, ECM/DMS, wikis, CRM/ERP, data warehouses and APIs. It normalises content, extracts entities, applies taxonomy and classification, and de-duplicates records. Hybrid indices (vector and lexical) support semantic search with precise filtering, while lineage and citations ensure every fact is traceable to source. Optional knowledge graphs model relationships between people, entities, cases and policies for richer answers and cross-document reasoning.

Bring your own and third-party agents into flows using open protocols. Cloud-Dog supports Model Context Protocol (MCP) tools and agent-to-agent (A2A) collaboration so internal and vendor agents can interoperate safely inside governed workflows. Tasks are routed to search agents, data agents, web agents or domain specialists as appropriate — while controls, audit and approvals remain consistent.

Use the right model for each task via policy-based routing and Mixture-of-Experts (MoE). Combine local small LLMs for private steps with cloud LLMs for scale; choose instruction-tuned, code, domain-specific, vision or embedding models per step. Rerankers and corpus-specific embeddings maximise retrieval fidelity and answer quality.

The platform embeds naturally into the corporate desktop. Users can ask questions from Teams or Slack, draft documents in Office/Google Workspace with citations, or trigger research from ticketing and CRM systems. Sidebars and add-ins surface context, suggested prompts and approvals. Outputs route to channels, emails, tasks or knowledge bases with role-aware templates and automated reviews.

Cloud-Dog supports safe augmentation with the web through controlled connectors: allow-listed domains, enterprise proxies, site adapters and retrieval policies that fetch only approved sources. Content is cached with provenance, watermarked and evaluated before use. PII masking, redaction and content safety filters protect users and data.

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Architecture

RAG Agent orchestrates multi-stage retrieval and generation processes, providing connectors to model providers (OpenAI, Ollama, Hugging Face) and enterprise data systems including SQL databases, document repositories and vector databases.

Storage and Data Management are handled via the integrated DataStore, offering encrypted document and object storage for structured and unstructured data. The Vector Indexer supports semantic and keyword-based searches, enabling rapid retrieval across large knowledge bases.

Each RAG Agent operates within a controlled environment to manage a specific data or model task — ensuring precision, traceability and isolation of workloads. The platform includes administration interfaces, security controls, monitoring dashboards and modular connector frameworks.

Optional specialised agents extend the platform: search agents for corpus-aware retrieval and re-ranking; data agents for SQL/warehouse joins and transformations; and web agents for compliant external sourcing via allow-listed domains and enterprise proxies. Agents can be first-party or third-party and integrated using MCP for tools and A2A for agent collaboration.

Comprehensive content inspection, boundary controls and information diodes secure knowledge and RAG flows with external services and third-party content, ensuring detection and mitigation of inappropriate context, malicious code, inappropriate access and unauthorised data exposure across the retrieval and generation workflow.

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Key Capabilities

Ingest and Index — Securely ingest from fileshares, email, ECM/DMS, wikis, SQL and APIs. Normalise formats, extract metadata, classify content and de-duplicate. Build vector and lexical indices for hybrid retrieval with configurable chunking, embeddings, redaction and retention policies.

Search and Retrieve — Run confidential, context-aware search across structured and unstructured sources. Combine semantic similarity, keyword filters and SQL joins. Apply access controls at query time and return grounded passages with citations, confidence scores and provenance.

Generate and Compose — Draft summaries, briefs, policies, FAQs, emails, reports and responses using the right models and tailored flows. Ground every output on retrieved sources, enforce prompt/response policies and embed citations automatically.

Agent Collaboration — Coordinate specialised agents for retrieval, reasoning, enrichment and actions. Chain tools for classification, entity extraction, transformation and notifications. Enable targeted research workflows that collect evidence, evaluate quality and produce traceable findings.

Workflow Design — Design multi-step flows using the visual builder. Parameterise templates, schedule or trigger by events and integrate approvals. Version workflows, track runs, capture metrics and roll back safely.

Audit and Governance — Maintain end-to-end lineage from prompt to source. Log prompts, tokens, retrieval sets, decisions and outputs. Enforce data minimisation, redaction, retention and export controls.

Security and Compliance — Apply SSO, RBAC/ABAC, network controls, encryption at rest/in transit and policy guardrails. Support data residency, sovereignty and segregation across tenants and environments.

Agent Ecosystem and Integrations — Integrate first-party and third-party agents into governed workflows using MCP tools and A2A collaboration. Compose search, data and web agents with domain specialists.

Model Orchestration — Route tasks to the most suitable LLM based on policy, cost, privacy and performance. Combine local small models with cloud LLMs and domain-specific, vision and embedding models.

Context and Boundary Inspection — Protect users and data with pre/post inference inspection, jailbreak filtering, classifiers and redaction. Enforce context boundaries, allow-lists and watermarking.

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Use Cases

1. Confidential Document Indexing — Securely index, search and retrieve internal documents.
2. Legal Knowledge Search — Query case files and policy records contextually.
3. Internal Research Assistant — Aggregate data into structured, factual summaries.
4. Policy Generation — Create accurate content from internal sources.
5. Compliance Review Automation — Automate evidence retrieval and audit preparation.