Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    24/7 Smart Customer Support RAG Bot

    1

    Design and deploy grounded RAG support workflows with citation-aware responses and automated escalation paths.

    $19.99

    Secure checkout via Stripe

    Money-back guarantee

    Converts to your local currency at checkout

    Security scanned
    24/7 Smart Customer Support RAG Bot

    24/7 Smart Customer Support RAG Bot

    Example session with this skill installed

    I need to build a RAG-based support bot for my Shopify store. I have PDFs for shipping, returns, and a CSV of FAQs. How should I structure the retrieval and what should the widget do?

    • Read your context and instructions
    • Compiled the 24 7 smart

    I have designed a RAG architecture for your Shopify support bot, focusing on groundedness and policy accuracy. The system prioritizes your shipping and return PDFs as primary authority over the FAQ CSV to ensure legal compliance.

    Shopify Support RAG Architecture

    Knowledge Base Plan

    SourcePriorityMetadataFreshnessNotes
    Shipping Policy (PDF)1 (High)category:logistics, region:globalDate-stampCore authority for transit times
    Return Policy (PDF)1 (High)category:legal, scope:refundsVersion 2.1Essential for eligibility checks
    Product FAQs (CSV)2 (Medium)category:general, type:q&aWeekly syncUse for general product queries

    Retrieval Configuration

    ComponentRecommendationReason
    Indexing StrategySmall-to-large chunkingPreserves context for specific policy clauses
    Retrieval TypeHybrid (Keyword + Semantic)Keyword for SKU/Policy IDs; Semantic for intent
    RerankingCross-Encoder RerankerEnsures top-1 result has highest policy relevance
    Confidence Threshold0.82 (Starting point)Prevents guessing on ambiguous refund queries

    Widget UX Design

    • States: Greeting with "Track Order" / "Start Return" chips.
    • Grounded State: Displays answer with "Source: Shipping Policy p. 3".
    • Uncertain State: "I can't confirm that shipping detail. Would you like to speak with an agent?"
    • Escalation: Direct CTA to Zendesk/Shopify Inbox when confidence < 0.8.

    Next steps

    1. Extract text from PDFs and clean out legacy header/footer noise.
    2. Implement a metadata filter to separate 'Refund' vs 'Exchange' logic during retrieval.
    3. Run an evaluation set of 20 "unanswerable" questions to tune the relevance threshold.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Customer support bots often hallucinate policies, ignore shipping exceptions, or provide outdated information when documents conflict. Standard RAG implementations frequently fail to handle the "no-answer" state, leading to fabricated answers that create legal and operational liability.

    What it does

    • Designs source-aware retrieval workflows that ground every response in approved FAQs, terms, and documentation.
    • Defines metadata-driven conflict resolution to prioritize the latest policy versions and region-specific rules.
    • Establishes explicit confidence controls to trigger "no-answer" or escalation states when evidence is insufficient.
    • Provides a comprehensive chunking and indexing strategy that preserves the integrity of legal and technical language.
    • Maps out customer intent routing to separate informational support from authenticated transactional actions.

    Frameworks & tools

    Compatible with vector databases (Pinecone, Weaviate, Milvus), embedding models (OpenAI, Cohere), and orchestration frameworks like LangChain or LlamaIndex.

    Why this beats prompting it yourself

    A simple prompt can't manage document versioning, region-based priority, or complex chunking strategies. This skill provides a systematic architecture for reliability, ensuring the agent knows when to stop talking and start escalating, which is critical for business compliance.

    Use cases

    • Automating product documentation and technical troubleshooting for SaaS platforms.
    • Grounding e-commerce support bots in shipping, refund, and warranty policies.
    • Managing multi-version internal playbooks for human support agent enablement.
    • Building help-center widgets that cite specific source articles for every claim.

    Known limitations

    Cannot guarantee zero hallucinations. Requires an existing indexed knowledge base or structured documents to function effectively.

    Requires pre-processed knowledge sources. Does not perform actual vector database hosting or document OCR. Effectiveness depends on the quality and freshness of provided documentation.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill goes through the trust checks below.

    Security scanned

    Checked by our 8-point scan before listing

    Fresh listing

    Recently published to Agensi

    Money-back guarantee

    Doesn't do what its page promises? You get your money back.

    Trust & safety

    Security scan

    Security scan passed 1 month ago

    Permissions

    No special permissions declared or detected

    Listed1 month ago

    What's inside

    Frequently Asked Questions

    This skill provides a systematic framework for building support bots that ground every answer in your specific documentation, ensuring the AI only speaks based on approved sources and knows when to escalate to a human.

    You will receive the complete workflow architecture, including specialized indexing strategies, metadata-driven conflict resolution logic, and pre-configured intent routing paths for your support automation.

    The skill is designed to be framework-agnostic and works seamlessly with popular vector databases like Pinecone or Milvus, as well as orchestration tools like LangChain and LlamaIndex.

    While the logic is designed to handle document updates via metadata priority, you will need an existing indexed knowledge base or structured documents (like FAQs or PDFs) for the agent to reference.

    Unlike basic prompting, this skill implements region-specific rules and version control logic to ensure the agent prioritizes the most recent or geographically relevant policy when documents conflict.

    This skill includes explicit confidence thresholds and "no-answer" states that automatically trigger escalation paths to human agents when the documentation doesn't provide a clear solution.

    New in LLM Ops