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AI Conversation Quality Review Agent Development
Conversations reviewed against your standard
An agent that reads the conversations your team and your other agents have with customers.
An agent that reads the conversations your team and your other agents have with customers. It checks each one against a rubric you wrote, quotes the lines behind every finding and prepares coaching notes for a team lead to review.
Quality review is done on a sample because nobody can read everything. A team lead listens to five calls a month per person and hopes they were typical. The complaint that mattered was in a conversation nobody reviewed, and the feedback people receive depends on which five calls were picked. Reading every conversation is the easy part to automate. Judging it fairly is not.
This page describes a workflow we propose to build as a custom project. It does not exist as software yet. It would read the transcripts and action logs our demonstrations already produce for every conversation. The rubric scoring, the review queue and the coaching reports are not built.
Six jobs it carries through
01Rubric check
Reviews each conversation against your criteria.
- Uses a rubric you wrote and can edit
- One finding per criterion
- Marks what could not be judged
- Applies the same rubric to people and agents
02Evidence for every finding
Shows why.
- Quotes the lines behind each finding
- Links to the moment in the transcript
- Separates fact from judgement
- Says when the evidence is thin
03Risk flags
Raises conversations that need a person now.
- A complaint that was not logged
- A promise outside your policy
- Personal data shared without a check
- An upset customer left without a next step
04Accuracy check
Compares answers with your approved information.
- Finds statements about policy and price
- Checks them against approved sources
- Flags answers with no source
- Notes outdated sources that were used
05Coaching notes
Prepares feedback for a team lead.
- Strengths and gaps per person
- Two or three examples each
- Suggested focus for next time
- Written for the lead to edit
06Trend report
Shows what is changing across conversations.
- Recurring questions with no approved answer
- Criteria that fail most often
- Differences between channels
- Changes since the last period
How One Request Moves Through
An example of one case, in 6 steps. The same steps play in the picture at the top of this page.
Conversation closed
A support chat ends. Its transcript and the record of actions taken are available for review.
Rubric applied
Each criterion is checked. Greeting and identity check pass, and the policy answer is questioned.
Evidence quoted
The finding cites the line where a refund was promised and the policy sentence that says otherwise.
Flag raised
Because a promise was made outside policy, the conversation goes to the team lead today.
Notes prepared
At the end of the week the lead receives notes per person with examples to discuss.
Lead decides
The lead edits the notes, removes one finding as unfair and shares the rest.
How the Work Is Done Today
A sample of five
Most conversations are never reviewed, including the ones that went wrong.
Feedback without evidence
"Be more empathetic" with no example the person can recognise.
Reviewers who disagree
Two leads score the same call differently, and the rubric is in their heads.
Problems found a month later
A wrong answer is repeated for weeks before a review catches it.
Agents marking their own work
An AI agent's conversations are assumed fine because nobody reads them.
Scores that become targets
People learn the checklist and stop listening to the customer.
Walk One Real Case Through It
Thirty minutes, one case from your own process. We map it against the workflow on this page and tell you plainly what can be reused from our demonstrations and what has to be built for your systems.
It Prepares the Review, a Person Gives the Feedback
Judging someone's work is a decision about a person. The agent reads everything and prepares findings with the evidence. A team lead reads them, removes what is unfair and decides what is said. Nothing from this agent goes to an employee file or a customer on its own.
Recording and reviewing conversations is regulated differently by country and by sector. The rules on notice, consent and retention are yours to set, and the agent works inside them.
- A rubric you wrote and can change
- A quoted line for every finding
- Flags go to a person
- Feedback given by a lead
- No ranking of individuals
- Review rules follow your policy
Where It Connects
The systems named here are the kind this agent works with. Which ones it connects to in your case is settled in the scope.
Transcripts
- Chat and messaging logs
- call recordings turned into text
- email threads
Rubric
- Your quality criteria, written down and versioned
Knowledge
- The approved information answers are checked against
Ticketing
- Where urgent flags are raised
People data
- Team structure, so notes reach the right lead
Reporting
- Your dashboards or a periodic report
Asked Before the First Call
Does it score employees?
It checks conversations against criteria and prepares findings with evidence. It does not produce a ranking of people, and nothing reaches an employee without a team lead reading it first. How review results are used in performance decisions is for you and your HR policy to decide.
How is this different from the conversation agents themselves?
Those agents take part in conversations. This one reads conversations afterwards, including theirs, and never speaks to a customer. It is the check on the others: it finds the answer with no approved source and the promise that should not have been made.
What does it need to work?
Transcripts, a written rubric and your approved information. Call recordings must be turned into text first, which needs a speech service tested in your languages. Most teams start with chat and email, where the text already exists.
What exists today?
Not as software yet. This page describes a proposed workflow. It would read the transcripts and action logs our demonstrations already produce for every conversation. The rubric scoring, the review queue and the coaching reports are not built. A project starts by mapping your process, then proving the first steps on test data before anything touches a live system.
Start With a Rubric and Fifty Conversations
If the rubric is not written down yet, that is the first step. We will compare the agent's findings with your own review of the same fifty.