Feedback
AI analytics for customer feedback
The voice of the customer is scattered across calls, tickets, surveys, and reviews. Monthly reports count volume. They rarely say which product, site, or shift keeps showing up. This agent clusters the words customers actually use and ties each theme to a SKU, job, plant, or supplier so operations can see a pattern before it becomes a formal claim.
WORKFLOW
How the system runs
Calls, tickets, and reviews are embedded, clustered, then written into a weekly operations brief.
Channel
Tickets
Channel
Reviews / surveys
Channel
Call notes
Agent
Cluster agent
Vector DB
Embedding store
Memory
Theme memory
Integration
SKU / plant map
Write-back
Weekly brief
Human
Ops review
01 / 06 · Ingest tickets, reviews, call notes
- Channel
- Agent
- Memory
- Vector DB
- Integration
- Human
- Write-back
How it runs
01
Gather the channels you already have
Tickets, call notes, surveys, reviews, and sales follow-ups. We start with two sources that are messy and real, then add the rest.
02
Cluster in the customer's language
Themes come from the text, then your team names the ones that match how the plant talks. A 'leaker' and a 'seal failure' can be one issue or two, and you decide.
03
Attach an owner
Each theme points at a SKU, lot, site, line, or trade. Volume without an owner stays a word cloud. Volume with an owner becomes a weekly list.
04
Publish a short brief
The output is a brief your quality, ops, or project lead can read in ten minutes: what rose, what fell, and which examples to open.
Where it shows up
Closeout and warranty comments
Owner punch comments, hotline calls, and survey notes group by trade and building, so the project team sees repeat issues before the warranty year piles up.
Print, seal, and freight themes
Distributor emails and QA returns cluster by structure, plant, and ship lane. A scuff problem that looks random in the inbox shows up as one press and one carrier.
Field comments on a product family
Service notes and portal tickets roll up by family, plant, and failure phrase, so engineering sees the wording customers use, not only the code on the claim form.
Questions we get
Is this a dashboard of star ratings?
Ratings are included when you have them. The useful part is the text: what people name, which product or site it attaches to, and whether the same story is arriving through tickets and calls.
Do we have to clean the data first?
No. We expect typos, forwarded threads, and half-filled forms. The first weeks are spent on mapping fields, not on a data-warehouse project.
Can the model invent a theme?
New clusters are proposals. A person accepts the name, merges it, or rejects it. We keep the examples under each theme so you can audit the group.
How fresh is the brief?
Most teams start weekly. Daily is reasonable once the sources are stable and someone is assigned to read it.
Does this replace a voice-of-customer program?
It gives that program a feed. Interviews, site visits, and executive calls still matter. This stops the written record from sitting unread.
Related reading
Related agents
- Customer ticket support
Triage the inbox, draft the reply, and route by plant, product, or severity.
- Warranty and quality-claim intelligence
Group returns and defects by SKU, line, supplier, and site.
- AI voice agents
Answer order, delivery, and scheduling calls, and hand a person the ones that need a decision.
Industries: Construction, Packaging, Manufacturing, Financial services, Retail and e-commerce, Food and beverage, Real estate, Healthcare, Travel and hospitality, Education