24/7 AI Support Without the Night Shift: FAQ Automation for Your Business

Opening hours. Pricing. How long does delivery take? What happens after I submit my request? For your customers, these are important questions. For you, they’re the same four sentences you’ve typed for the twentieth time this week.

A full support inbox isn’t an anomaly. It’s a pattern — and patterns can be automated.

The problem with repetitive questions

Answering support requests manually creates a dilemma: fast responses require time that’s needed elsewhere, but slow responses frustrate customers — even when the answer is simple.

The frustrating part: most of these questions are predictable. For many small businesses, 70–80% of incoming enquiries revolve around the same topics. The knowledge to answer them already exists — it just isn’t in the right system yet.

That’s exactly where the FAQ Assistant comes in.

What is RAG — and why does it matter?

The FAQ Assistant is built on a technique called RAG: Retrieval-Augmented Generation. In three sentences:

Instead of programming all knowledge into an AI model directly, you give it a searchable knowledge base. When a question comes in, the AI first searches for the most relevant entries — and then replies based on those sources. The result is more accurate and stays up to date, because you only maintain the knowledge base, not the model.

Think of the difference between an employee who memorises everything and one who looks things up carefully — the second is often more reliable.

How the workflow works

The process runs in four steps:

Customer asks a question
    │
    ▼
Question is embedded
(OpenAI Embeddings → numeric vector)
    │
    ▼
Retrieve closest matching entries
from the knowledge base
    │
    ▼
Claude formulates a reply
based on the retrieved sources
    │
    ├── High confidence ──→ Send reply directly
    │
    └── Low confidence  ──→ Escalate to a human

Embedding sounds technical, but it’s the core of how this works: every question and every knowledge entry is converted into a numeric vector. Similar meanings produce similar numbers. This lets the system recognise that “When are you open?” and “Weekend opening hours?” are asking the same thing — even though the wording is different.

Safety through confidence scoring

Not every question is clear-cut. What happens when the AI is uncertain?

For each reply, the assistant internally scores how well the retrieved knowledge entries match the incoming question. If this score falls below a defined threshold, the request isn’t answered automatically — it’s escalated: to a human, a ticketing system, or a defined fallback address.

This isn’t a weakness. It’s the design. An assistant that knows what it doesn’t know is more trustworthy than one that always invents an answer.

Demo: Try the FAQ Assistant

What this means in practice

A look at concrete time savings:

Task	Manual	With FAQ Assistant
Answer a pricing enquiry	5 min	~3 sec
Clarify onboarding questions	10 min	~3 sec
Recurring support questions	Daily	Automatic
Response time	Business hours	24/7

At five recurring enquiries per day, five days a week, that’s over 200 hours saved per year — for questions whose answers have been the same for months.

GDPR: what happens to the data

Support requests can contain sensitive information. Transparency isn’t optional here:

• EU data storage: the vector database runs on Supabase in a European region. No data leaves the EU without your authorisation.
• No logging without consent: requests are used for processing, not stored permanently — unless you explicitly enable that.
• Full control over the knowledge base: you maintain what the assistant knows. No black-box behaviour: you see every entry and can change or remove it at any time.
• DPA with Anthropic: AI processing via Claude is covered by a Data Processing Agreement, keeping you GDPR-compliant.

What you can customise

The FAQ Assistant is not an off-the-shelf product — it adapts to your operation:

• Your own knowledge base: whether it’s an FAQ document, a Notion page, or existing help text — the ingestion workflow handles all common formats.
• Channel choice: integrate into a chat widget on your website, an email inbox, or Slack — wherever your customers write to you.
• Escalation routing: low-confidence requests land directly in your ticketing system (Freshdesk, Linear, Jira — whatever you use).
• Tone and style: a short prompt document is enough for the AI to match how you write.

Download the workflows

The FAQ Assistant consists of two n8n workflows:

• Reply workflow: processes incoming questions and returns answers
  Download faq-assistent.json

• Ingestion workflow: reads your knowledge base and writes it into the vector database
  Download faq-ingestion.json

After importing into your n8n instance and setting up a Supabase database, you’ll be up and running within one to two hours. The SQL setup for the vector database is available in the GitHub repository.

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Interested in a custom FAQ solution for your business? Get in touch.