Workflow Automation and RAG Agent System for Canal Digital

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Canal Digital required a scalable, reliable AI-driven system to automate workflows and enable intelligent knowledge retrieval across customer-facing and internal operations. The challenge was not just automation, but controlled intelligence—ensuring responses were accurate, explainable, and grounded in verified data.

I designed and implemented a Workflow Automation and Retrieval-Augmented Generation (RAG) Agent System using n8n, custom Python scripting, and a structured knowledge layer to deliver enterprise-grade AI assistance without compromising reliability or control.

Customer and internal teams relied on fragmented documentation (manuals, FAQs, tickets, internal guides).

Manual workflows slowed response times and introduced inconsistencies.

Traditional AI chatbots hallucinated or produced unreliable answers when operating without constraints.

There was a need for automation + intelligence, not just AI output.

I built a RAG-first architecture orchestrated through n8n, combining automation, data pipelines, and AI agents into a single cohesive system.

Workflow Orchestration (n8n): Automated ingestion, routing, triggering, and monitoring of workflows across systems.

Custom Python Logic: Data preprocessing, validation, transformation, and embedding pipelines to ensure clean, structured inputs.

RAG Agent System: AI agents retrieve answers strictly from approved knowledge sources before generating responses.

Knowledge Layer: Structured and unstructured documents indexed for semantic search and contextual retrieval.

Controlled AI Output: Guardrails applied to prevent hallucinations and ensure traceability of responses.

User query or system trigger initiates the workflow in n8n

Relevant data sources are identified and retrieved

Python scripts preprocess and enrich the data

The RAG agent performs semantic retrieval from the knowledge base

The AI generates responses grounded strictly in retrieved content

Outputs are delivered to customer support interfaces or internal dashboards

Reduced response time for customer support and internal queries

Higher answer accuracy through retrieval-grounded AI responses

Scalable automation without increasing operational overhead

Enterprise-safe AI behavior aligned with compliance and trust requirements

Improved consistency across customer-facing communications

n8n (Workflow Automation)

Python (Custom Logic & Data Processing)

Retrieval-Augmented Generation (RAG)

Vector-based Semantic Search

AI Agents with Guardrails

This project demonstrates how AI systems should be built in real-world enterprise environments: automation first, intelligence second, and control always.

Rather than deploying unconstrained AI, this solution proves that well-orchestrated, risk-aware AI systems outperform generic chatbots in reliability, trust, and long-term value.