This article provides a complete, practical walkthrough of the Splitout Filter Automate Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Building an AI HR Assistant: A No-Code n8n Workflow for BambooHR Company Policies and Benefits Chatbot  
   
   Meta Description:  
   Discover how to build an AI-powered HR assistant chatbot that uses BambooHR and OpenAI to help employees understand company policies, benefits, and find relevant contacts—all with no code using n8n.  
   
   Keywords:  
   n8n workflow, BambooHR chatbot, AI HR assistant, company policies chatbot, OpenAI GPT chatbot, employee directory automation, Supabase vector store, no-code HR automation, LangChain, AI knowledge assistant  
   
   Third-party APIs Used:
   
   - BambooHR API  
   - OpenAI API  
   - Supabase API  
   - LangChain (via n8n-langchain nodes)
   
   ---
   
   Article:
   
   In today’s fast-paced remote and hybrid work environments, Human Resources departments are expected to provide timely answers to employees about policies, benefits, and internal procedures. Doing this manually doesn’t scale—especially in growing organizations. One solution? Deploy an intelligent HR assistant that can do the work for you.
   
   This article introduces a no-code approach to this challenge using n8n, an open-source automation platform. We break down a real-world workflow that builds an AI-powered company policies and benefits chatbot, customized to work with BambooHR data and powered by OpenAI’s GPT models.
   
   🎯 What the Workflow Does
   
   This n8n workflow builds a dynamic HR assistant chatbot that:
   - Combines documents from BambooHR (like the employee handbook, 401k policies, and benefits PDFs)
   - Stores and retrieves data using Supabase as a vector store
   - Uses OpenAI embeddings and GPT chat models to interpret employee queries
   - Provides intelligent, structured responses based on company policies
   - Has the ability to find and suggest contact persons (such as department leaders or supervisors) by calling an AI-enhanced employee lookup tool
   - Handles ambiguous or incomplete employee queries with logic-based fallbacks
   
   Let’s break this down step-by-step.
   
   🧠 Step 1: Ingest Company Files into a Vector Database
   
   The HR assistant relies on documents that are already uploaded into BambooHR. These include things like:
   - Company handbook
   - Expense policies
   - Benefits overviews
   - Retirement plans (401k, etc.)
   
   Using the BambooHR API, files from a category named “Company Files” are gathered. A filter ensures only PDF documents are used. Each PDF is then downloaded and split into text chunks using LangChain’s RecursiveCharacterTextSplitter. These chunks are then transformed into vector embeddings via OpenAI's Embedding API, and stored in Supabase as a vector store collection called company_files.
   
   This creates a searchable knowledge base optimized for natural language queries.
   
   💬 Step 2: Set Up the AI Chatbot Interface
   
   Employees can initiate conversations with the chatbot via the “Employee initiates a conversation” node, which triggers a LangChain-powered HR AI agent. This agent is configured with:
   
   - An AI memory buffer to remember context
   - OpenAI chat models for LLM communication
   - A vector store tool to retrieve policy documents from Supabase
   - A tool called employee_lookup_tool, which searches BambooHR for specific people or departments
   
   The AI assistant’s system prompt defines a clear operating protocol. For example, when an employee asks for someone to talk to about benefits, the assistant will:
   
   1. Search the document vector store for a relevant contact.
   2. Check if that contact has contact info. If not, use the employee_lookup_tool.
   3. If no contact exists, fallback to suggesting the most relevant senior person in HR—again using the tool and OpenAI.
   
   This layered approach ensures a thoughtful and accountable path to escalation or support.
   
   🔍 Step 3: Implement an Employee Lookup Tool
   
   At the heart of the chatbot’s “human” knowledge is a tool called employee_lookup_tool. This tool queries BambooHR to:
   - Look up full details of a named employee (job title, email, supervisor)
   - Retrieve the most senior person in a department if only a department name is given
   
   Why is this so important? Because people often ask HR chatbots questions like:  
   - “Who should I talk to in payroll about my missing reimbursement?”  
   - “Can you give me the contact for someone in HR about my 401k?”  
   
   The AI assistant uses classification (with textClassifier) and prompt chains to determine if the input is a person or department. If it's a department, the tool uses OpenAI in a clever fallback path to identify leadership using job titles. BambooHR doesn’t support parametric search within its API, so n8n pulls all employees on each request and filters locally.
   
   💻 Tech Stack at a Glance
   
   Here’s what’s powering this AI assistant under the hood:
   - OpenAI API: GPT-3.5/4 models for chat and embeddings
   - BambooHR API: File downloads and employee info
   - Supabase: Vector store to store embeddings for semantic search
   - LangChain: A framework to power memory, chat logic, and tool management
   - n8n automation platform: No-code orchestration of every interaction
   
   🔥 Key Benefits
   
   - Fully automated onboarding and HR support
   - Accessible 24/7 to all employees
   - Instant access to company documentation and escalation paths
   - Reduces load on HR staff and improves efficiency
   - Custom fallback paths ensure no dead ends
   
   🧩 Optional Additions
   
   This workflow can be expanded further:
   - Integrate Slack or Microsoft Teams for chatbot delivery
   - Expand document categories (e.g., IT policies, onboarding docs)
   - Use search ranking or feedback scoring for chatbot answers
   - Add analytics to see which policies are being asked about most
   
   🙋‍♂️ Who Built This?
   
   The workflow was created by Ludwig Gerdes. You can connect with him on [LinkedIn](https://www.linkedin.com/in/ludwiggerdes).
   
   —
   
   In summary, this no-code, LLM-powered HR chatbot built on n8n shows just how far automation and AI tools have come. By leveraging internal HR platforms like BambooHR, document vectorization with LangChain, and GPT reasoning from OpenAI, organizations can now deploy an always-on HR assistant without code and without compromise.
   
   This is not just HR automation—it’s HR Augmentation.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.