Gladly AI Agent QA: VoC, AutoQA Insights
- Oversai connects Gladly conversations, customer history, channels, topics, inboxes, handoffs, and Gladly AI activity to monitor service quality.
- VoC helps CX teams see where customer effort, complaints, sentiment, product friction, and unresolved needs appear across the conversation history.
- AutoQA and QA Insights show whether Gladly AI workflows need better answers, guides, escalation logic, coaching, or ownership.
Gladly is designed around personal customer service. Conversation history, channels, customer context, routing, reports, and AI-assisted self-service all shape how a customer experiences the brand. Gladly AI adds another layer, which means QA must look beyond resolution and handoff counts.
Oversai connects to Gladly as an intelligence layer for VoC, AutoQA, and QA Insights. Gladly remains the customer service platform. Oversai helps teams classify customer signal, score AI and human conversation quality, and route findings to the people who can improve the experience.
What The Gladly Integration Does
Oversai analyzes approved Gladly conversations, customer history, channels, inboxes, topics, classifications, handoff states, AI activity, timestamps, agent context, selected customer attributes, and reporting dimensions.
The integration turns Gladly service interactions into structured VoC and quality workflows.
| Gladly signal | Oversai output |
|---|---|
| Conversations, channels, and customer history | VoC topics, sentiment, customer effort, complaints, and risk flags |
| Gladly AI assists, resolutions, and handoffs | AutoQA scores, escalation quality, grounding, and recovery opportunities |
| Topics, inboxes, classifications, and teams | QA Insights by workflow, issue type, queue, owner, and segment |
| Guides, answers, and handoff patterns | Knowledge gaps, automation tuning needs, and root-cause alerts |
Gladly documents Gladly AI as an AI and automation platform for customer service teams that works with Gladly Team across digital channels: What is Gladly AI. Gladly also documents a Gladly AI dashboard for resolutions, handed-off conversations, guides used, and performance review: Gladly AI Dashboard.
For the broader platform overview, read Gladly AutoQA, VoC, and QA Insights.
Prerequisites
Start with a Gladly admin, QA leader, support operations owner, Gladly AI owner, and owners for product, policy, retention, ecommerce, or escalation workflows.
Prepare the Gladly scope, AI criteria, and owner rules before routing alerts:
- Gladly channels, inboxes, topics, classifications, customer attributes, handoff states, and AI workflows
- A VoC taxonomy for complaints, sentiment, customer effort, product friction, unresolved needs, churn risk, and policy confusion
- An AutoQA scorecard for accuracy, empathy, resolution, escalation, compliance, tone, and customer-history use
- AI-agent criteria for grounding, guide fit, handoff timing, answer completeness, refusal behavior, and recovery quality
- Ownership rules for coaching, answer updates, guide changes, routing fixes, policy work, product feedback, and customer recovery
Useful internal pages include Gladly QA + VoC, Gladly QA Insights integration, and Oversai pricing.
Setup Steps
A focused Gladly rollout should begin with one high-volume AI-assisted workflow.
- Select the first Gladly scope. Choose the channels, topics, inboxes, customer segments, classifications, and Gladly AI workflows to include.
- Authorize access. A Gladly admin approves the connection and required permissions.
- Map metadata. Oversai maps conversation content, channel, topic, inbox, classification, handoff, customer context, AI activity, and timestamps.
- Configure VoC. Define topics for complaint language, effort, product friction, policy confusion, unresolved questions, churn risk, and negative sentiment.
- Configure AutoQA. Define human-agent and AI-agent criteria, including grounding, guide fit, handoff timing, empathy, escalation, and resolution quality.
- Assign owners. Route findings to supervisors, QA leads, Gladly AI owners, answer managers, operations, product, policy, or retention owners.
- Calibrate examples. Review actual conversations and handoffs before expanding so findings are specific enough to act on.
The first rollout should answer which Gladly AI conversations look successful in reports but still create customer effort or business risk.
What VoC Looks Like Once Connected
Once connected, Gladly becomes a continuous Voice of Customer source.
Oversai classifies customer language into topics, sentiment, complaint signals, customer effort, product friction, unanswered questions, policy confusion, and churn risk. Because Gladly keeps the customer relationship close to the conversation, leaders can connect those signals to customer history and service context.
For AI-assisted workflows, VoC shows whether automation changes the customer's experience. A resolved conversation may still contain frustration. A handed-off conversation may reveal a missing answer. A topic that appears contained may still create repeat contact when the guide does not match the customer's real problem.
What AutoQA Looks Like Once Connected
AutoQA evaluates Gladly conversations against the standards your team defines.
For human agents, criteria may include accuracy, empathy, ownership, documentation, escalation, policy adherence, and resolution. For Gladly AI, teams can evaluate answer grounding, guide fit, handoff timing, completeness, safe refusal, customer-history use, and recovery quality after escalation.
Oversai can flag high-risk combinations, such as a low AutoQA score plus negative sentiment, a handoff after repeated customer effort, a policy-sensitive answer with weak grounding, or an AI-assisted resolution followed by a reopened conversation.
What QA Insights Look Like Once Connected
QA Insights connect Gladly quality outcomes to operating context.
Supervisors can compare quality by channel, topic, inbox, classification, team, AI workflow, handoff path, guide, customer segment, and time period. Leaders can see whether quality problems come from coaching gaps, weak answers, unclear guides, routing friction, policy ambiguity, or product issues.
Examples:
- Gladly AI resolves simple order questions but hands off subscription exceptions late
- One guide appears frequently in low-score conversations
- A topic has high resolution volume but rising negative sentiment
- Handoffs include too little context for agents to recover quickly
- Customers in one segment use language that the current classification misses
Example Use Cases
Gladly teams use Oversai to:
- Monitor Gladly AI and human-agent conversations with one QA model
- Detect customer effort and complaint themes before surveys catch up
- Prioritize review for handed-off, reopened, negative-sentiment, or high-value conversations
- Identify which answers, guides, and topics need tuning
- Route VoC and AutoQA alerts to coaching, operations, product, retention, or AI owners
For adjacent planning, read Gladly QA Insights integration and AI agent escalation rubric.
Bottom Line
Gladly gives CX teams a customer-first workspace for conversations and AI-assisted support. Oversai adds the intelligence layer for AI-agent QA, VoC, AutoQA, and QA Insights.
Together, Gladly and Oversai help teams move beyond resolution counts toward continuous quality monitoring that explains which AI, human, knowledge, and workflow changes will improve customer experience.
Frequently Asked Questions
Does Oversai replace Gladly AI?
No. Gladly remains the customer service platform and AI workflow. Oversai analyzes approved Gladly data to provide VoC, AutoQA, QA Insights, and AI-agent quality monitoring.
What Gladly data can Oversai analyze?
Oversai can use approved conversations, channels, inboxes, topics, classifications, handoff states, AI activity, timestamps, customer history, selected attributes, and reporting dimensions depending on scope.
Why do Gladly AI workflows need AutoQA?
Resolution and handoff metrics do not fully explain quality. AutoQA helps teams evaluate answer accuracy, guide fit, grounding, customer effort, escalation timing, and recovery quality.