Reamaze QA

Support quality, training and agent certification in one system

Support is the last point where an order can still be saved or quietly lost. Reamaze QA reads every conversation the way the most experienced service lead would, only every day and across the whole flow. And it doesn’t stop at the verdict: the gap it finds is closed by a lesson and a test.

The problem

By hand, a QA manager gets through 5-7% of conversations a day, and the store loses money in exactly the percent nobody looked at. The lead notices a problem days later, once it has already collected returns and reviews. Meanwhile standards sit in documents nobody checks against, and training rests on a verbal briefing and the new hire’s memory.

What Reamaze QA does

Scores every conversation - tone, completeness, policy compliance, objection handling, missed sales. The whole flow is reviewed rather than a sample, and the cost stays predictable no matter how the volume grows.
Calibrates to your brand - the lead adjudicates the disputed verdicts and those decisions become rules. With every round the system scores closer to the way you score, not against some abstract politeness rubric.
Analytics for the team and each agent - an “agent × violation” heatmap answers the real management question: fix the process, or have a conversation with one person.
Topics and trends - every request is classified by topic and folded into a group of similar problems, and the current window is compared against the weeks before it. The system surfaces what is growing: a wave of delivery complaints or confusion around a new product shows up at the start, before it becomes mass and turns into returns and one-star reviews.
Workload by hour and by day - you see when the queue peaks and when the line sits idle. The lead staffs shifts against the real flow instead of guessing: where people are short, backup goes in; where there are too many, paid hours don’t burn. The shift schedule lives in the same system, so the decision doesn’t have to be carried anywhere.
Team training - courses built from lessons, the mandatory ones kept apart, progress stored lesson by lesson. Materials are written where the team works, so updating a standard waits on nobody.
Tests and certification - each with its own pass threshold and an “agent × course” summary. You see not pass or fail but what exactly wasn’t understood: when half the team fails the same question, the standard is the problem, not the people.
One workspace - the shift schedule and the SOP wiki sit where the scores and the training are. No agent juggles five tabs or asks in chat where to look for things, and access is split by role: everyone sees their own.

Training that doesn’t take people off the line

A new agent doesn’t wait for someone to free up and teach them. They walk the same path as the rest of the team: courses, examples from your store’s real cases, a test at the end. What they come out with is the same knowledge an experienced colleague has, not a retelling from whoever sat next to them on shift. Nobody has to be pulled off their own work to run the onboarding, and that is usually the expensive part: two people leave the line instead of one.

From there the system works for the whole team. The mistakes it sees in the conversations show exactly where knowledge is thin, in the new hire and in the person who has been here three years. When the same mistake collects into a group, what to reinforce is no longer the lead’s guess but a targeted lesson. The agent takes it at a convenient time without stepping away from work, and the lead sees the knowledge picture per person: who passed, with what score, and where the gap is still open.

Any change rolls out the same way: a new product, a new refund policy, a new tool in the workflow. Instead of a meeting half the team missed and an email half the team never opened, they get a course and a test. Everyone takes it when it doesn’t break their shift, and the lead sees in the report who already works by the new rule and who doesn’t yet.

The AI that holds the standard

The system doesn’t score in the abstract: it scores against your brand’s rules and the cases already adjudicated, so a verdict doesn’t drift week to week and holds up in a conversation with the agent. What the transcript cannot settle is checked against store data instead of guessed: orders and payments come from Shopify and CheckoutChamp over their APIs. The system reads 100% of closed conversations, folds up the day in the evening, and in the morning the lead gets a finished report in Telegram: how many were reviewed, how many came back clean, and which conversations need attention, with the rule, a quote from the dialogue and a link right in the chat. Anything left hanging without a response gets its own reminder. When it fits, a page with PayPal disputes from PayPal Dispute Assistant sits right next to them.

AI at work: reads every conversation → shows where the team falls short, and courses and tests close exactly those gaps.

In development: a check before the reply goes out

Quality control looks backwards by nature: the conversation is over, the verdict is in, the lessons are for tomorrow. The next tool works the other way, before the message reaches the customer. A browser overlay reads the reply the agent has just written, checks it against your SOP and points out what is missing: an unanswered question, a skipped refund policy, a tone that will work against you in this particular situation. Simple replies are drafted by the AI itself. A mistake is cheaper to prevent than to review: in a support conversation it rarely stays just a mistake - it is a return, an order that never happened, a customer who won’t write again but will write a review. A tense moment is defused where it appears, inside the conversation itself, before irritation becomes a conflict, and the conflict becomes lost money and a dent in the store’s reputation.

The last word stays with the person. The agent sees a suggestion, not an auto-send: they take it, edit it or ignore it. That is how the main weakness of language models is handled - the confidently stated invention. The model proposes; the decision belongs to whoever answers for the result. The tool is in development and testing now.

Your data stays in your environment

Conversations, scores, courses and test results live on our servers, separate from every other client’s data. Only what scoring cannot do without ever leaves, and that data is not used to train anyone else’s AI. Training materials and agent statistics never leave the environment at all.

How it starts

We need access to your helpdesk and your service rules; where no written standard exists, we build one from your own conversations. Setting up a brand takes hours, not weeks. After that the system runs on its own, and calibration happens on your own disputed cases.

This is not a shared account you sign up for: the system is installed separately for each client, with its own rules, courses and tests matched to the product and the processes. It works on a subscription: deployment, updates and support stay on our side. Write to us and we will show it running on your own conversations.

Stack

Python, FastAPI; Claude models, RAG retrieval of rules and precedents, API integrations: Reamaze, Shopify, CheckoutChamp, Telegram.