Most UK service businesses collect customer feedback — NPS surveys, Google reviews, post-project questionnaires, exit interviews — and then do very little with it. The data sits in a survey tool dashboard or an email inbox, reviewed quarterly at best. An AI customer feedback agent changes that. It reads every piece of feedback as it arrives, classifies sentiment, extracts recurring themes, flags the signals that need human attention, and sends a weekly digest to your desk — so you are never more than seven days behind on what your clients actually think. Here is exactly how to build one.
Why Customer Feedback Stays Unanalysed
The typical UK service business collects feedback from three to five sources simultaneously. There is the NPS survey that goes out after each project. A Google Business Profile accumulating reviews. A quarterly client satisfaction questionnaire. Post-call email replies. Exit interview notes for churned clients. The problem is not shortage of data. It is that nobody owns the analysis job.
Reading, tagging, and synthesising twenty pieces of qualitative feedback takes two to three hours of focused effort. At fifty responses a month, that is a half-day task that sits on nobody's formal job description. So it does not happen. The NPS scores get glanced at. The reviews get spot-checked when someone thinks to look. The themes that appear repeatedly — slow response times, unclear scope, communication gaps — go unnoticed until they surface in a lost client.
The most damaging feedback is not the one-star review your team sees and responds to. It is the three-star review with a detailed concern that nobody reads until the client is already planning to leave.
Research from customer retention specialists shows that up to 65% of client churn is preventable when warning signals are identified within four weeks. Most UK service businesses identify them four months later, when the client is already gone. The gap is not a strategy problem — it is a systems problem. An AI customer feedback agent closes it.
What Your AI Customer Feedback Agent Will Do
Before building, it helps to be clear on what the agent does. There are five components working in sequence.
Feedback ingestion. The agent monitors all your feedback sources continuously. Typeform and Google Forms via webhook. Google Business Profile reviews via API polling. Email feedback via an email trigger in n8n. Each new piece of feedback enters the pipeline as a structured payload — source, timestamp, client name if available, and raw text.
Sentiment classification. A Claude Haiku model reads the feedback text and outputs a sentiment score from one to ten, a sentiment label (positive, neutral, negative), and an urgency flag — true if the feedback signals an active problem rather than a general comment. This takes roughly 500–800 tokens per feedback item. At Haiku pricing, under a fraction of a penny each.
Theme extraction. Alongside sentiment, the model extracts up to three themes from each piece of feedback, drawn from a predefined taxonomy you define for your business: communication, delivery speed, pricing, scope clarity, team knowledge, onboarding experience, and so on. Over time, these become your signal layer — the data that tells you what is actually degrading before clients say it directly.
Alert routing. Feedback scoring below five with an urgency flag triggers an immediate Slack direct message to the relevant account owner. The message includes the client name, source, sentiment score, the key quote from the feedback, and a one-line suggested action. No dashboard to check. No delayed report. The account owner knows within minutes.
Weekly digest. Every Monday at 08:00, a scheduled node aggregates the past seven days of classified feedback, calculates average NPS trend, counts by sentiment category, extracts the top recurring themes, and generates a plain-English summary paragraph. The digest lands in a Slack channel or via email — and takes under two minutes to read.
Step-by-Step: Building the Agent in n8n and Claude
The full build takes two to three weeks, depending on how many feedback sources you are integrating. Here is the step-by-step sequence.
Step 1: Set Up Your Feedback Input Triggers
Start with your highest-volume feedback source. If you use Typeform, configure a Webhook node in n8n pointing to your Typeform survey — every new response fires the webhook and starts the pipeline. For Google Forms, use the Google Sheets trigger (Forms responses land in a linked Sheet) and set it to fire on new rows. For email feedback, use n8n's Gmail or Outlook trigger filtered to a specific label or folder you designate for client feedback.
Google Reviews require a polling approach. Set up a Schedule trigger that runs every four hours and calls the Google Business Profile API to fetch new reviews since the last run. Store the timestamp of the last poll in a simple Airtable row or Google Sheet cell so the agent knows what is genuinely new versus what it has already processed.
Step 2: Build the Sentiment and Theme Classifier
Add an AI Agent node configured with Claude Haiku. The system prompt instructs the model to act as a feedback analyst for your specific business — include a one-paragraph description of your service type and client base so the model has the context to classify accurately. The user message passes the raw feedback text.
The required output is a JSON object with four fields: sentiment_score (1–10), sentiment_label (positive, neutral, negative), themes (array of up to three strings from your predefined taxonomy), and urgency_flag (boolean). Use structured outputs — define the JSON schema and force the model to match it. This eliminates parsing errors and makes downstream routing reliable. If the classifier returns a schema mismatch, the workflow retries once before logging the error for review.
Step 3: Create the Alert Router
Add an If node after the classifier that evaluates two conditions simultaneously: sentiment_score is five or below AND urgency_flag is true. When both are met, a Slack node sends a formatted direct message to the relevant account owner.
The Slack message template should include the client name, the feedback source, the sentiment score, a direct quote from the feedback (keep it under 200 characters for readability on mobile), and a single suggested action based on the urgency. Keep the message scannable — account owners receive this mid-day and need to be able to act on it in under thirty seconds.
All classified feedback — not just urgent items — is logged to a Google Sheet with timestamp, source, sentiment score, sentiment label, and themes. This sheet becomes the data source for your weekly digest and, over time, your trend analysis. The AI client health monitor can pull from this sheet to build a richer per-client risk picture alongside engagement signals and invoice patterns.
Step 4: Build the Weekly Digest
Add a separate workflow triggered by a Schedule node set to every Monday at 08:00. This workflow reads all feedback logged in the past seven days from your Google Sheet, passes the dataset to a Claude Haiku summarisation node, and outputs a structured weekly summary.
The summarisation prompt should ask for: average sentiment score across the week, count by sentiment label, the top three recurring themes with frequency counts, and a plain-English paragraph summarising the week's feedback in the voice of a senior colleague briefing the team. The paragraph should highlight anything unusual — a spike in negative feedback, a theme appearing for the first time, a particular client flagged multiple times.
Deliver the digest to a dedicated Slack channel or via email. Include a link to the full data sheet for anyone who wants to drill down. The whole digest takes under two minutes to read and replaces the quarterly review process that rarely quite happens.
Connecting the Agent to Your AI Operating System
The feedback agent is most powerful when connected to your broader AI operating system rather than running in isolation. Four integrations are worth building once the core agent is stable.
Content input. Recurring positive themes in feedback — "clear explanations", "proactive communication", "deep industry knowledge" — are the topics your clients value most. Wire the weekly digest output into a content brief that feeds your AI LinkedIn content agent. Your content calendar becomes anchored in what clients actually care about, not what you guess they value.
Health monitoring. Route negative feedback events — any item scoring five or below — to your AI client health monitor as a signal input. A single critical review is noise. Three in six weeks from the same client is a pattern worth acting on. The health monitor can weight this alongside email response rates and invoice payment timing to give a composite health score per client.
Testimonial requests. Feedback scoring nine or ten triggers a separate workflow: a personalised email asking the client if they would be willing to contribute a brief testimonial or case study. The request goes within 24 hours of the positive feedback — when sentiment is highest. Conversion rates on this approach are typically 40–60% higher than a generic quarterly testimonial campaign sent cold.
Training data. Every classified feedback item, with its verified labels, becomes a training asset for future agent improvements. After three to six months of operation, you have a labelled dataset that can be used to fine-tune classification for your specific client base. This is the AI knowledge moat principle applied at the data layer: your agent becomes more accurate over time because you own the data it learns from.
What It Costs and What It Returns
For a typical UK service business processing 50–200 feedback items per month, the API costs for this agent run between £25 and £45 per month. The classification and summarisation work is handled almost entirely by Claude Haiku — fast, cheap, and entirely sufficient for the task. The alert routing and digest generation add negligible cost on top.
The build investment — in n8n with Claude — is two to three weeks of configuration and testing. Apply the evaluation framework after the first two weeks: test the classifier against a set of feedback items you have already reviewed manually, verify the urgency flag accuracy against known cases, and confirm the alert routing fires correctly before going live with client-facing data.
The return is less about the hours saved on manual analysis — roughly six to eight hours per month — and more about what the analysis enables. Early identification of churn signals preserves client relationships that would otherwise end quietly. One retained client at a typical UK consultancy retainer of £3,000–£8,000 per month covers the agent's running costs for years. The feedback agent does not save money by doing less. It makes money by keeping clients you would otherwise lose.
If you want to work out whether a feedback agent is the right first build for your operating system — or whether there is a higher-value automation to start with — book a free 30-minute call. We will map your current feedback sources, identify the highest-risk gaps in your client monitoring, and give you a build plan that fits your team.