RAG Knowledge Chatbot
An AI chatbot trained on your documentation, product catalog, and FAQs. Your customers get instant, accurate answers 24/7 -- your team stops answering the same questions.
How RAG Works
Retrieval-Augmented Generation Flow
Chatbot Capabilities
What I Build
A RAG chatbot isn't just Q&A — it's knowledge management, conversation design, and intelligent routing working together. Here's every capability I deliver.
Knowledge Base Ingestion
Upload PDFs, web pages, Google Docs, or CSV files — the chatbot indexes everything and keeps answers current.
- Multi-format document ingestion
- Automatic re-indexing on update
- Version-aware knowledge management
Natural Language Q&A
Customers ask questions in plain English (or Spanish) and get accurate, concise answers sourced from your data.
- Plain language question handling
- English & Spanish auto-detection
- Source-attributed answers
Multi-Source Retrieval
The AI searches across all your documents, product specs, and policies to find the most relevant answer.
- Cross-document context assembly
- Semantic similarity matching
- Confidence-scored responses
Conversation Memory
The chatbot remembers context within a conversation so follow-up questions work naturally.
- Multi-turn conversation tracking
- Context-aware follow-up handling
- Session history logging
Escalation Logic
When the bot isn't confident or detects frustration, it hands off to a human instantly.
- Configurable confidence thresholds
- Automatic frustration detection
- Seamless human agent routing
Analytics Dashboard
See what customers ask most, satisfaction rates, unanswered questions, and conversation volumes.
- Most-asked question reporting
- Unanswered question gap analysis
- Conversation volume & trends
Deliverables
What You Get
Custom AI Trained on YOUR Data
Not a generic chatbot. Your product specs, policies, procedures, and institutional knowledge become the AI's source of truth.
Website Embed Widget
A sleek chat widget that drops into your website with a single script tag. Matches your brand colors and positioning.
FAQ & Documentation Ingestion
Upload PDFs, web pages, Google Docs, or CSV files. The chatbot indexes everything and keeps answers up to date.
Product Catalog Knowledge
The AI knows your products -- specs, pricing, availability, compatibility. Customers get accurate product answers instantly.
Multi-Language Support (EN/ES)
The chatbot responds in English and Spanish automatically based on user language, expanding your reach to more customers.
Analytics Dashboard
See what customers ask most, satisfaction scores, unanswered questions, and conversation volumes. Data-driven chatbot improvement.
Scope & Pricing
Every Project Is Different
I scope every engagement individually based on your data, goals, and timeline. No cookie-cutter packages — just a solution built around what you actually need.
Data Volume
Number of SKUs, pages, or data points to process drives the scope of the project.
Delivery Format
Raw CSV, live dashboard, API integration, or automated reporting — each has different complexity.
Ongoing Support
One-time delivery versus ongoing monitoring, maintenance, and iteration on results.
Process
How It Works
Gather
Collect your knowledge base: product catalogs, FAQs, policies, specs, and support tickets.
Index
Chunk, embed, and store your documents in a vector database for fast semantic retrieval.
Build
Create the chatbot interface with RAG pipeline, confidence thresholds, and human handoff logic.
Test
Run real-world test scenarios, edge cases, and adversarial inputs. Verify accuracy against known correct answers.
Deploy
Launch with monitoring, conversation analytics, and scheduled knowledge base refreshes.
Deep Dive
Expert Insight
RAG Architecture: How It Actually Works
RAG (Retrieval-Augmented Generation) solves the biggest problem with AI chatbots: making up answers. Instead of relying only on what the AI was trained on, RAG works in three steps: (1) Convert your documents into embeddings (mathematical representations of meaning) and store them in a vector database. (2) When a user asks a question, convert it to an embedding and find the most similar documents. (3) Pass those documents as context to the AI along with the question — the AI answers using your data, not its training data.
The result: accurate, source-grounded answers that update instantly when you update your documents. No fine-tuning, no retraining, no hallucinations about products that don't exist.
Chunking Strategy: The Make-or-Break Decision
How you split your documents into chunks determines answer quality. Too large (full pages) and the AI gets diluted context. Too small (individual sentences) and it loses important surrounding information. For product catalogs, chunk by product — each product's specs, description, and FAQs in one chunk. For policies, chunk by section. Overlap chunks by 10-15% to avoid losing context at boundaries. This single decision affects accuracy more than which AI model you choose.