Every day customers tell support what is not working. UpstreamW reads a whole batch of those conversations at once and shows what the main problems are, why they happen, and which cases a person should look at. People then decide what to fix.
The journey of a conversation
From a pile of conversations to a clear picture
Upload the conversations.A file with a batch of support conversations, for example the last month.
Hide personal details, on our own computer.Names, emails, phone numbers, card and account numbers, tax IDs and amounts are replaced by labels like [NAME_1]. The real values never leave.
Safety check.If anything personal is still visible, that conversation is stopped and a person handles it. Nothing is sent.
Sort each case.An AI service reads only the hidden version. It picks the customer's main pain from a fixed list and scores where the cause sits: People, Process, Policy or Product.
Summary.The main pains, the cause behind each one, real customer quotes, and the cases a person should look at.
People decide.The team reads the summary, adds what they know from their own work, and writes the recommendation. The tool does not decide.
Where the cause sits
The 4Ps
People
How the person who helped the customer handled it.
The agent gave the wrong steps, so the customer came back.
Process
The steps and handoffs behind the scenes.
The case waited for another team to act.
Policy
A rule or a limit.
A spending limit the cardholder could not see.
Product
The app or the platform.
The app kept showing "pending" without saying why.
Safety rules
What always happens
Personal details never leave our computer. Only the hidden version is sent, and a final check stops anything that slipped through.
Sensitive cases always go to a person. Fraud, charges the customer does not recognize, locked accounts and legal threats.
The AI cannot invent categories. It picks from a fixed list. Anything new is marked Emerging for a person to name.
People make the decisions. The tool gives faster answers and the evidence. The recommendation is written by the team.
Honest limits
What it does not do yet
Tested on made-up cases
60 realistic but invented conversations. Real conversations need a careful test first.
Causes are a starting point
The main pain is right about 98% of the time in the test. The cause matches the reviewed label a little over half the time, so people check it.