Gladly QA Insights Integration: VoC and AutoQA
- Oversai connects to Gladly conversations, customer timelines, channels, topics, and selected profile context so CX teams can analyze quality and customer signal.
- VoC turns Gladly customer history into themes, sentiment, effort drivers, repeat-contact patterns, and root-cause evidence.
- AutoQA scores interactions at scale, while QA Insights show where leaders should coach, route alerts, and fix workflows.
Gladly gives service teams a customer-centered view of support. Instead of treating every message as an isolated ticket, teams can see conversations, history, channels, and profile context around the customer.
That is valuable for CX. It also makes quality harder to evaluate manually. A reply can look acceptable in one conversation but still miss prior history, repeat an answer the customer already rejected, or fail to escalate after several contacts.
Oversai connects to Gladly as an intelligence layer for VoC, AutoQA, and QA Insights. Gladly remains the agent workspace. Oversai helps leaders understand the quality, risk, and customer signal inside the interactions already happening there.
What The Gladly QA Insights Integration Does
Oversai analyzes approved Gladly conversations and selected customer context, then structures that data for quality, customer feedback, and CX operations.
Depending on scope, Oversai can work with customer messages, agent responses, channel data, inboxes, topics, timestamps, conversation status, handoffs, customer profile attributes, and selected timeline events.
| Gladly signal | Oversai output |
|---|---|
| Customer conversations | AutoQA scores, evidence, sentiment, topic classification |
| Timeline and profile context | Repeat-contact patterns, customer effort, unresolved history |
| Inboxes, topics, and channels | Trends by queue, workflow, issue type, and segment |
| Handoffs and escalations | QA Insights, risk alerts, coaching opportunities |
Gladly documents REST APIs for customer profiles, timelines, conversation data, and integrations: Gladly API. Gladly also documents ways to work with conversation content for external systems: Ingest conversation contents.
For the broader integration overview, read Gladly AutoQA, VoC, and QA Insights.
Prerequisites
Start with a Gladly admin, QA leader, support operations owner, and CX leader. The first decision is scope. Choose one area where customer history changes the right answer.
Good starting points include VIP support, returns, delivery promises, subscription changes, account issues, complaints, escalations, and repeat contacts.
Prepare:
- The Gladly inboxes, channels, topics, teams, and customer segments to include
- The timeline events and profile attributes that should be available for reporting
- One QA scorecard for accuracy, empathy, resolution, ownership, policy, and escalation
- One VoC taxonomy for topics, sentiment, customer effort, complaints, root causes, and churn risk
- Data handling rules for account, order, payment, health, identity, or regulated information
Useful internal pages include Gladly QA + VoC, Oversai AutoQA, and Oversai pricing.
Setup Steps
A strong Gladly rollout starts narrow enough to calibrate.
- Select the first Gladly scope. Choose the inboxes, channels, topics, customer segments, and workflows Oversai should analyze first.
- Authorize access. A Gladly admin approves the integration access needed for the selected data.
- Map metadata. Oversai maps channel, inbox, topic, status, customer attribute, timeline event, handoff state, and timestamp into reporting dimensions.
- Configure AutoQA. QA leaders define criteria for accurate answers, empathy, policy adherence, ownership, resolution, and escalation judgment.
- Configure VoC. CX leaders define themes such as delivery friction, product issues, billing confusion, policy complaints, account risk, and repeated effort.
- Calibrate examples. Review a sample set with QA, operations, and supervisors before expanding coverage.
- Route QA Insights. Send repeat-contact risk, weak handoffs, policy friction, and coaching opportunities to the right owners.
The first rollout should answer a practical question, such as "Which customer histories create avoidable effort?"
What VoC Looks Like Once Connected
Once connected, Gladly becomes a continuous Voice of Customer source.
Oversai classifies customer language into topics, sentiment, complaints, product issues, unresolved history, churn signals, and customer effort. Because Gladly keeps customer context close to the conversation, VoC can show more than isolated themes.
A CX leader can see whether high-value customers are contacting support multiple times about the same promise. A product leader can see repeated defect language before survey results arrive. An operations leader can see whether one policy creates avoidable handoffs across channels.
What AutoQA Looks Like Once Connected
AutoQA evaluates Gladly interactions against the standards your team defines.
Common criteria include whether the agent understood the current issue, acknowledged relevant history, answered accurately, used the right tone, followed policy, resolved the need, documented the next step, and escalated at the right moment.
Oversai can also flag conversations for human review. That helps with complaints, VIP customers, regulated topics, refund pressure, repeated contacts, negative sentiment, and risky handoffs.
AutoQA gives reviewers broader coverage and better evidence for calibration, exceptions, coaching, and process fixes.
What QA Insights Look Like Once Connected
QA Insights connect Gladly quality results to customer context.
Supervisors can compare quality by inbox, team, channel, topic, segment, workflow, status, and handoff path. CX leaders can see whether low scores line up with repeat contacts, negative sentiment, unresolved history, or specific policy friction.
Examples:
- A VIP workflow has fast replies but weak escalation judgment
- A return process creates repeated contacts after a policy answer
- A support team shows strong empathy but misses prior timeline context
- One topic drives negative sentiment across chat and email
- A complaint pattern appears first in customer history before surveys catch up
QA Insights help teams improve the system behind the conversation, not only coach one agent after one review.
Example Use Cases
Gladly teams often use Oversai to:
- Score more customer conversations than sampled QA can cover
- Detect repeat-contact drivers from history and conversation text
- Audit escalations, complaints, returns, subscriptions, and policy-sensitive interactions
- Prioritize human QA review by risk, sentiment, and customer value
- Connect VoC themes to profile attributes, timeline events, and support workflows
- Share evidence with product, operations, retention, and leadership teams
Bottom Line
Gladly gives CX teams a customer-centered support workspace. Oversai helps leaders move from sampled QA and delayed survey feedback to continuous VoC, AutoQA, and QA Insights across the customer relationship.
Frequently Asked Questions
Does Oversai replace Gladly?
No. Oversai works above Gladly as a VoC, AutoQA, and QA Insights layer. Agents can keep working in Gladly while leaders analyze customer signal and support quality in Oversai.
What Gladly data can Oversai analyze?
Oversai can analyze approved conversations, customer messages, agent responses, channels, inboxes, topics, statuses, timestamps, handoffs, profile attributes, and selected timeline events depending on scope.
Why is Gladly useful for QA Insights?
Gladly keeps customer history close to the conversation. QA Insights can therefore connect quality issues to repeat contacts, prior history, customer attributes, and workflow context.