D $2,497–$4,497 · fixed SLA · 10–21 days

AI-powered chatbot or agent workflow in n8n — RAG pipeline, vector DB, multi-channel, production-hardened.

Automations built to last.

Your team has documents everywhere — PDFs in Google Drive, runbooks in Notion, policies in Confluence. Your staff spends hours searching for answers. Your customers email support with questions already answered in your docs.

I build an n8n-native RAG pipeline that ingests your documents, chunks and embeds them into a vector database, and wires a chatbot that answers questions from your actual knowledge base — with citations, confidence thresholds, and human handoff when the AI doesn't know.

No discovery call. You send the brief with your doc sources and preferred chat channel. I send a written scope confirmation. You fund the milestone. I ship.

NDA-friendly · Fixed-price · Money-back guarantee

Send a brief Ask what's included

FIG · RAG chatbot pipeline

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Fit

Who this is for

You run an SMB with institutional knowledge scattered across PDF manuals, Notion wikis, Confluence spaces, or Google Drive folders.

Your support team answers the same 40 questions every week — questions already answered in existing documentation that nobody can find fast enough.

Or you want a lead qualification chatbot on your website that handles the first interaction intelligently, asks the right questions, and routes qualified leads to your sales team — without a human sitting in the chat queue 14 hours a day.

Or you inherited a half-built chatbot that hallucinates, doesn't cite sources, and has no fallback to a human when it doesn't know the answer. You want it rebuilt properly.

Deliverable

What you receive

A complete RAG-powered agent system running on n8n, delivered as a production-ready ZIP with everything below.

Document ingestion pipeline

PDF, web page, Notion export, Confluence export, or Google Drive sync → chunking (configurable chunk size + overlap) → embedding generation (OpenAI or Cohere) → upsert into your vector database. Incremental — re-run adds new docs without re-processing the entire corpus.

RAG query workflow

User question → semantic search against vector DB → top-k retrieval with relevance scoring → LLM synthesis (Claude or GPT, configurable) → answer with inline citations referencing the source document and page. The prompt is tuned to refuse hallucination — if the corpus doesn't contain the answer, the bot says so.

Chat interface integration

Single-channel in Tier 1: Slack, WhatsApp, Telegram, or a website chat widget (Chatwoot or custom). Multi-channel in Tier 2 — same RAG backend, multiple frontends. Each channel respects its platform's formatting, rate limits, and message-size constraints.

Fallback handling + human handoff

Confidence thresholds configured per deployment. Below threshold → the bot acknowledges uncertainty and routes the conversation to a human (Slack channel, email, or helpdesk ticket). No silent failures, no confident-sounding hallucinations.

Tool use (Tier 2)

The agent can call external tools — look up order status, check inventory, query a database, create a ticket — via n8n sub-workflows wired as callable tools. Each tool is documented and sandboxed.

Monitoring + DLQ

Failed queries land in a dead-letter queue with the original question, retrieval context, and error preserved. Monitoring hooks on query latency and failure rate. Alerts to your channel of choice.

Runbook

Plain English. How to add new documents. How to update the embedding model. How to tune the confidence threshold. How to debug a bad answer. How to rotate API keys.

Scope exclusions

What's NOT included

Training or fine-tuning custom ML models. This uses pre-trained LLMs (Claude, GPT-4) via API.
Fine-tuning LLMs on your data. RAG retrieval is the architecture — not fine-tuning.
Mobile app development. The chat interface is Slack, WhatsApp, Telegram, or web widget — not a native iOS/Android app.
Real-time voice. Text-based chat only. Voice-to-text pre-processing is a v2 add-on.
Multi-language support beyond English. Translation layer is a v2 scope item.
Hosting infrastructure. You bring the n8n instance and the vector DB (Pinecone, Qdrant, Weaviate, or Supabase pgvector). I configure and populate them.
Ongoing operations. For monthly oversight see SKU I — Retainer.

Process

How it works

01

You buy.

$2,497 (Tier 1) or $4,497 (Tier 2). Fund the milestone via Upwork or direct invoice.
02

You send the brief.

Document sources (Google Drive folder, Notion workspace, PDF uploads, Confluence space URL). Preferred chat channel. Preferred LLM (Claude or GPT — I'll recommend if unsure). Any tool-use requirements (Tier 2).
03

I send a written scope confirmation within 48 hours.

Locked list of what's being built, document sources included, channel(s) wired, and the delivery date.
04

I build.

Ingest your documents. Configure the RAG pipeline. Wire the chat interface. Test against your actual corpus. Mid-build written check-in at 50% completion.
05

I deliver the ZIP.

n8n workflow JSON exports, ingestion scripts, runbook PDF, and a short Loom walking through a live demo against your documents.

Pricing

Tier	Scope	Price	Timeline
Tier 1 — Single-channel RAG bot	One chat channel, document ingestion, RAG query, fallback handling, monitoring, runbook	$2,497	10 days
Tier 2 — Multi-channel + tool use	Multiple chat channels, tool-use agent, everything in Tier 1	$4,497	21 days

Fixed price per tier. No hourly drift. The scope confirmation locks what's in and what's deferred before you fund the milestone.

Questions

FAQ

Which LLM do you use?

Claude (Anthropic) or GPT-4 (OpenAI) — your choice. Both work well for RAG. Claude tends to be more cautious about hallucination out of the box; GPT-4 has broader tool-use ecosystem support. I'll recommend based on your use case in the scope confirmation.

Which vector database?

Pinecone (hosted, easiest), Qdrant (self-hosted, no vendor lock-in), Weaviate (self-hosted, strong filtering), or Supabase pgvector (if you already use Supabase). I'll recommend based on your existing infrastructure and document volume.

What latency should I expect?

Typical end-to-end: 2–5 seconds from question to answer. Bottleneck is the LLM generation step. Retrieval from vector DB is sub-200ms. I optimize chunk size and top-k to minimize token usage without sacrificing answer quality.

What are the ongoing costs?

LLM API costs (typically $50–$300/month depending on query volume), vector DB hosting ($0–$70/month depending on provider and corpus size), and your n8n instance. The runbook includes a cost estimation worksheet.

Is my data private?

Your documents stay in your vector database. Queries go to the LLM API provider you choose (OpenAI or Anthropic). Both offer data-processing agreements and zero-retention API tiers. I do not retain copies of your documents after delivery.

Can I add more documents after delivery?

Yes — the ingestion pipeline is designed for incremental runs. Drop new PDFs in the source folder (or add pages to your Notion workspace) and trigger the ingestion workflow. New documents get chunked, embedded, and added without re-processing the existing corpus.

What if the chatbot gives a wrong answer?

The RAG architecture cites sources for every answer, so users can verify. The confidence threshold is tunable — lower it to be more conservative (more handoffs to humans, fewer wrong answers). The DLQ captures every low-confidence or failed query for review. The runbook covers the feedback loop for improving retrieval quality over time.

Can this replace my entire support team?

No — and I won't sell it that way. This handles the repeatable, documentation-answerable questions (typically 40–60% of support volume). The rest routes to your human team via the handoff mechanism. The goal is deflection of routine queries, not full automation of judgment calls.

Ready to build your AI agent ?

NDA-friendly · Fixed-price · Money-back guarantee

Async. Written. RAG pipeline + chat interface in 10–21 days.

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syed@noorflows.com · async only · UTC+5