Customer Support and Voice AI
AI receptionist for small business: calls, bookings and handoffs
What an AI receptionist does on a real call, the rules that apply, a sample greeting and a pass/fail checklist to launch one safely.
Support Operations
AI Support QA
Every closed conversation scored against your own quality rubric, with coaching points surfaced automatically.
Most support teams review only a small sample of conversations, so coaching rests on whatever a manager happened to read. This workflow scores every closed conversation against the quality rubric you define, covering tone, accuracy and policy adherence, and explains the reason behind each score. Managers get specific coaching points for each agent, delivered to email, Slack or a web dashboard.
- Every closed conversation scored against your own rubric
- Each score explains the reasons behind it
- Specific coaching points for every agent
$3,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Support QA reviews closed tickets and conversations against the quality rubric you define, scoring tone, accuracy and policy adherence, and highlighting specific coaching points for each agent. It delivers a dashboard or recurring report to one channel. Every score comes with the reasons behind it.
Why businesses buy it
Manual QA is slow and patchy: managers sample a handful of tickets, scores vary by reviewer, and agents receive feedback weeks after the conversation. Quality problems slip through until a customer complains. After launch, every conversation is scored consistently, patterns become visible across the team, and coaching focuses on real examples. The Scope Card sets the rubric, data sources and delivery channel before you order, so you know exactly how quality is measured and who receives the reports.
Problems this solves
Managers can only review a small sample of tickets each week
AI Support QA scores closed tickets and conversations against your own rubric for tone, accuracy and policy. Results arrive in one dashboard or recurring report, covering far more than a hand-picked sample.
Agents receive vague feedback that is hard to act on
Every score comes with the reasons behind it and specific coaching points for each agent. Team leads can run coaching sessions with real examples instead of general impressions.
Policy mistakes surface only after a customer complains
Policy adherence is part of the rubric, and up to five alerts can flag repeated misses. A lead can spot a pattern, such as a wrong refund rule, while it is still fresh.
A good fit if
You currently review only a small sample of closed tickets by hand, because reading them all is not realistic for your team.
You have a quality rubric, even an informal one, and want it applied the same way to every conversation, not just the ones a manager happens to read.
You want coaching points tied to specific tickets, not a vague monthly score with nothing an agent can act on.
You want trends over time, such as which issues are most often scored low, not a one-off snapshot.
Not the right fit if
You want AI helping agents while they are handling a ticket, not reviewing it afterward. See AI Support Copilot for in-conversation drafting.
Your real gap is finding tickets that are going wrong while they are still open, not scoring them after close. See AI Escalation Agent.
You want new help center content, not a quality score. See AI Support Knowledge Agent, which drafts articles from resolved tickets.
Your review needs span more than three read-only data sources or more than twenty-five defined metrics. Talk to us through our Custom AI enquiry form.
How it works
- The agent reads closed tickets and conversations from your connected helpdesk or data sources on the schedule you set.
- Each one is scored against the rubric you defined, up to twenty-five metrics, such as tone, accuracy, policy adherence, and resolution speed.
- Specific coaching points are pulled out for each conversation, for example a missed policy step or an unclear explanation, not just a number.
- Results are compiled into the dashboard or recurring report you chose, delivered to email, Slack or a web dashboard.
- Up to five alerts flag patterns worth immediate attention, such as a sudden drop in scores for one agent or one ticket type.
- Everything is checked against three months of your own historical data before launch, so scores reflect your real quality bar, not a generic one.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Read-only access to each data source you want reviewed, such as your helpdesk or call transcripts.
- Your quality rubric and definitions for each metric, even if it is currently informal.
- At least three months of historical conversations so the build can be checked against real, known outcomes.
- The report recipients and the channel you want results delivered to.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- Your business information (company basics, hours and brand voice), shared during intake.
- One decision-maker who can confirm scope questions during the build.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake reviewed, quality rubric and metrics confirmed.
Weeks 1–2
Scoring logic and coaching-point extraction built.
Week 3
Data sources connected and the delivery channel configured.
Weeks 3–4
Results checked against three months of your historical data.
Week 4
Your review, plus two rounds of requested changes.
Go-live
Live scoring closed tickets, recorded handoff session, support begins.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $3,900 |
| Express | 15–25 working days | $4,600 |
| Rush | 10–20 working days | $5,250 |
What you receive at handover
- AI Support QA configured and deployed in your environment, scoring against your rubric
- Workflow, prompt and guardrail configuration, with read-only connections to up to three data sources
- One dashboard or recurring report by email, Slack or web, plus up to five alerts
- A test report checking results against three months of your historical data
- An admin guide and the recorded 60-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Makes it easier to give every agent regular, specific coaching.
- Helps catch accuracy and policy issues before they repeat across many tickets.
- Tickets reviewed per week
- Compare the number your leads reviewed by hand before launch with the number the QA report covers each week after.
- Average QA score per agent
- Track each agent's rubric score in the report month by month, starting from the first full month.
- Reopened tickets
- Use your helpdesk's reopen report to compare the two months before launch with the two months after.
Before you start: Note how many tickets leads review by hand each week and your current reopen rate. Write down the rubric you use today, even an informal one.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is one-time. Running AI Support QA costs roughly $20 to $150 a month, covering AI model usage and hosting or scheduling. Higher ticket volume and more frequent reporting both push you toward the top of that range.
AI usage during the build and every month after runs through your own accounts, so the cost and the provider relationship both stay visible.
Ask about running costsCustom requirements
Need more than three read-only data sources, more than twenty-five metrics, or a report format this Scope Card does not cover? Add a note at checkout for a written Change Quote. Larger or bespoke projects go through the Custom AI request form instead.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes and change hours for work such as a new metric or an adjusted rubric weighting. It starts after the post-launch support period, bills monthly and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A QA scoring add-on bills per seat or per reviewed conversation | A custom agency quotes after discovery calls | Ours is one fixed price, shown on this page before you buy |
| Scope | A generic QA tool needs your rubric configured metric by metric by hand | An agency agrees scope during the project | Ours is written on the Scope Card before purchase |
| Delivery | A QA add-on can connect in days but scores against a generic default rubric first | An agency build usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Coaching detail | Most tools give a single overall score with little explanation | An agency build depends on what was agreed | Ours surfaces specific coaching points tied to each conversation |
Questions about this product
Does AI Support QA review every conversation or a sample?
Every closed ticket or conversation in your connected data sources, on the schedule you set, not just a manual sample. That is the main advantage over a manager spot-checking a handful each week. Scores and coaching points are produced consistently across your full ticket volume.
What does AI Support QA cost to run each month?
Roughly $20 to $150 a month, covering AI model usage and hosting or scheduling. Higher ticket volume and more frequent reports both sit toward the top of that range. You pay this directly in your own accounts, with no separate per-conversation fee from us.
How long does AI Support QA take to launch?
Delivery runs on a fixed schedule from a verified intake, and the exact date is shown in the delivery panel before you buy, with express and rush options if you need it sooner. All three include checking results against three months of your historical data before the QA scoring goes live for your team.
What access does AI Support QA need to my systems?
Read-only access to up to three data sources, typically your helpdesk and any call or chat transcript system. Nothing is written back to those systems. Credentials are shared by adding our team as a user or creating a key you control, never by email, and access can be revoked at any time you choose.
How do you know the quality scores are accurate before launch?
We check the build against three months of your own historical data and review the results with you before anything goes live. If scores do not match how your team actually judges quality, that feedback goes into one of the two included rounds of changes.
Can I change the quality rubric after AI Support QA launches?
Yes, through monthly maintenance. Its change hours cover work such as adding a metric or reweighting the rubric, and you can cancel any month if your reporting needs stay fairly stable. Share the updated rubric and metric definitions, even if informal. Maintenance also covers monitoring and fixes, and starts after the post-launch support period.
Can AI Support QA also draft replies for agents during a live ticket?
No, this build reviews conversations only after they have closed, scoring them against your rubric. If you want AI assisting agents while a ticket is still open, drafting replies and surfacing articles in real time, see AI Support Copilot, which is built specifically for that in-conversation, assistive role.
Guides on this topic
All guides Score every conversation, not just the ones a manager had time to read.
Order AI Support QA and every closed conversation is scored against your rubric, giving managers fair, consistent coaching points for each agent.
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