What it does
An AI agent does the work of a support QA engineer: every morning it pulls conversations closed in the past 24 hours, scores each reply against a fixed rubric, and assembles a report for the team lead. The goal of automation is to close the gap between the declared support standards and what actually reaches customers.
Step-by-step process
- Export from the helpdesk of conversations closed in the last 24 hours — at least 10% of the daily volume, stratified sample by agents and ticket categories.
- Running each conversation through the QA rubric: resolution accuracy, communication tone, script adherence, SLA compliance, classification tag correctness, response completeness.
- A score for each criterion on a scale and an overall conversation score with a supporting quote from the response text.
- Compiling the daily report: benchmark responses, responses with deviations, overall trends by agents and categories for the past week.
- Sending the report to the team lead via Slack or email with direct links to each ticket in the helpdesk for quick review.
- Repeating the cycle every business day without gaps and without 'forgot this Monday'.
QA rubric — what is checked
- Accuracy: whether the response actually resolves the customer's issue.
- Tone: whether it matches the brand's declared tone of voice.
- Scripts: whether approved phrasing is used for standard situations.
- SLA: whether the agent met the standards for first response time and ticket closure.
- Tags: whether ticket categories are correctly assigned for further analytics.
- Completeness: whether the issue is resolved without loose ends and implicit assumptions.
What automation does NOT do
- Does not replace live review. The AI agent flags responses that fall outside the rubric; the final judgment — why and what to do about it — remains with the team lead.
- Does not train agents in real time. The report shows what broke in the past 24 hours; coaching, script updates, and 1:1s are the manager's job, not the script's.
- Does not edit responses. Review covers already sent conversations; automation does not intervene during the exchange with the customer.
How it works
The architecture is built as a custom-code workflow with an LLM evaluator and direct integration into the helpdesk API. The central component is the evaluator, which takes the conversation text and a YAML description of the rubric as input and outputs a structured JSON with scores and supporting quotes for each criterion.
Technical flow
The script runs on a schedule, pulls data from the helpdesk, passes it through the LLM with a fixed rubric prompt, and writes the result to the reporting database. The model provides not only a score but also a quote from the conversation to support the rating — so the team lead does not have to dig into the question of 'why the AI decided this way.'
Solution components
Component | Role |
|---|---|
Helpdesk API | Source of closed conversations with metadata (agent, category, SLA) |
Scheduler | Runs the workflow daily in a fixed time window |
Sampler | Stratified 10% sample by agents and categories |
LLM evaluator | Rubric-based scoring, supporting quotes |
Storage | Score history for trends and auditing |
Reporter | Report compilation and delivery to Slack or email |
Implementation steps
- Rubric finalization. The Grow2.ai team together with the support team lead formalizes the existing quality criteria as YAML: for each item, a question and scale are defined. Without this step, automation makes no sense: the model checks what is written down, not what 'everyone knows in their head.'
- Helpdesk connection. A service token with read-only access to closed conversations for the selected period is created. The integration works with any helpdesk that has an API for exporting conversations.
- Evaluator calibration. The evaluator is run on a historical sample of conversations, and the results are compared against the team lead's manual scores. Discrepancies are reviewed, and the rubric and prompt are refined. The goal is alignment between the model's scores and the team lead's scores in the majority of cases.
- Sample configuration. The Sampler takes 10% of the daily volume and stratifies it: at least one conversation per active agent per week and at least one conversation per each main request category.
- Report format. The team lead and the Grow2.ai team agree on the structure of the daily email — what goes to the top, which metrics are in the summary, and which charts cover 7 and 30 days.
- Pilot launch. For two weeks the evaluator runs in parallel with manual auditing: this allows discrepancies to be caught and the rubric to be fine-tuned without risk to production.
- Transition to production. Manual auditing remains only for edge cases and escalations; routine checking transitions to automation.
How the model provides a reasoned score
The evaluator prompt is structured explicitly: first the model reads the rubric and the conversation, then for each criterion it extracts a specific quote from the agent's response, and only then assigns a score. This approach with supporting quotes reduces the likelihood of hallucinations and makes the score verifiable — the team lead sees the basis for the model's decision and can quickly agree or challenge the conclusion.
Prerequisites
Implementation requires minimal but specific infrastructure and team readiness.
Access and data
- Helpdesk API with read access to closed conversations — Zendesk, Intercom, Freshdesk, HelpScout, Front, or any system with a conversations endpoint.
- Closed conversation history for the past month in a volume sufficient for calibration (several hundred records).
- Current quality criteria in any form: a Google doc, a Notion page, or a verbal agreement from the team lead. The implementation team will handle formalization in YAML.
- A report delivery channel: a Slack workspace with permission to create a bot integration, or the team lead's work email.
Team readiness
- The support team lead is ready to allocate 4–6 hours in the first week for rubric definition and 2–3 hours per week during the first month for calibration.
- The support manager agrees that automation removes the routine of sampling and evaluation, but does not replace manual review of complex cases.
- Agents are informed about the transition to regular QA and understand that already-closed conversations are being reviewed, not real-time work.
Timeline
Full implementation takes 2–4 weeks:
- Week 1: rubric definition, helpdesk connection, first run on historical data.
- Week 2: evaluator calibration, report format alignment.
- Weeks 3–4: pilot in parallel mode with manual audit and transition to production.
After launch, automation runs without intervention; the Grow2.ai team remains on support for the rubric and prompts.
Pain points
- Review — bottleneck
- Inconsistent Quality
FAQ
How long will the launch take?
Full launch takes 2–4 weeks for a support team of 5–20 agents. Week 1 — defining the rubric and connecting to the helpdesk, week 2 — evaluator calibration, weeks 3–4 — pilot running in parallel with manual audit and transition to production. Timelines extend if the current quality criteria exist only in the team lead's head and need to be discussed and documented first.
We don't have a formalized QA rubric — is that a blocker?
No, the absence of a formal rubric is a normal starting point. In the first week, the Grow2.ai team runs a working session with the team lead, captures the existing criteria (by which responses are currently evaluated informally) and turns them into YAML. A separate rubric development project is not needed — everything fits within the overall implementation timeline.
What are the risks and what can break?
Three main risks. The first — divergence between model scores and the team lead's judgment in edge cases; resolved by calibration on a historical sample. The second — changes to the rubric without updating the YAML, causing the automation to evaluate against outdated criteria. The third — helpdesk API downtime; the evaluator logs errors and retries, but the automation is not responsible for the availability of a third-party service.
Does it work for our industry?
Suited for SaaS/Tech as the primary segment and universally applicable to any industry with text-based support channels — e-commerce, fintech, edtech, B2B services. The automation operates on conversation text and the rubric; the industry itself does not affect how the evaluator works. Industry specifics are embedded in the quality rubric and response scripts.
Can we check 100% of tickets instead of 10%?
Technically — yes, but this rarely adds value. A 10% stratified sample across agents and categories is statistically sufficient to catch systematic quality deviations. 100% is justified in regulated industries with compliance requirements — in that case, the volume of LLM calls and cost are recalculated against the actual daily conversation flow.
What about privacy and personal data in conversations?
Before sending to the LLM, the evaluator runs the conversation through a PII filter: emails, phone numbers, card numbers and customer identifiers are replaced with placeholders. For teams with GDPR requirements, processing in an EU region and log retention in compliance with the regulation are configured. Source conversations are stored on the helpdesk side and are not duplicated within the automation.
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