Gladly AutoQA, VoC, and QA Insights for CX Leaders
- Oversai connects to Gladly so CX teams can analyze customer conversations, timelines, and service context without replacing Gladly.
- AutoQA scores Gladly interactions for accuracy, empathy, resolution quality, policy adherence, and escalation judgment.
- VoC and QA Insights turn Gladly conversation history into customer themes, coaching priorities, and operational signals.
Gladly is built around the customer, not only the ticket. Conversations, channels, profile data, and history sit close to the customer record, which gives service teams a richer view of the relationship.
That model is valuable for modern QA and Voice of Customer programs. A single conversation might look resolved, but the customer history may show repeated contacts, a weak handoff, unresolved frustration, or a policy issue that keeps coming back.
Oversai connects to Gladly as an AI analysis layer for VoC, AutoQA, and QA Insights. Gladly remains the support workspace. Oversai helps leaders understand what is happening inside the conversations already taking place there.
What The Gladly Integration Does
Oversai analyzes Gladly conversations and selected customer context, then structures that data for QA, customer feedback analysis, and CX observability.
Depending on the approved scope, that can include customer messages, agent responses, channel data, inboxes, topics, timestamps, profile attributes, conversation status, handoffs, and selected timeline events.
| Gladly signal | Oversai output |
|---|---|
| Conversation content | AutoQA scores, evidence, sentiment, topic classification |
| Customer timeline context | Repeat-contact patterns, unresolved history, effort drivers |
| Inboxes, topics, and channels | Trends by queue, issue type, team, and customer segment |
| Handoffs and escalations | Risk alerts, coaching opportunities, process gaps |
Gladly's developer documentation describes REST APIs for customer profiles, timelines, conversation data, and integrations: Gladly API. Gladly also documents workflows for exporting and ingesting conversation contents: Ingest conversation contents.
Oversai uses that data to help QA, CX, and operations leaders measure quality and customer signal beyond manual review.
Prerequisites
Start with a Gladly admin or integration owner who can approve the connection. Then define the first workflow.
Good first scopes include VIP support, returns, delivery questions, account changes, subscription issues, complaints, escalations, or any queue where customer history affects the right answer.
Prepare these inputs:
- The Gladly inboxes, channels, topics, and customer segments to include
- The customer attributes and timeline events that matter for reporting
- A QA scorecard for accuracy, empathy, resolution, policy, and handoff quality
- A VoC taxonomy for topics, sentiment, root causes, effort, and churn risk
- Data handling rules for sensitive profile, order, or account information
Helpful internal pages include Gladly QA + VoC, Oversai AutoQA, and Voice of Customer analytics.
Setup Steps
A focused setup keeps the first rollout useful.
- 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 to read relevant conversation and customer context.
- Map metadata. Oversai maps channels, inboxes, topics, status, customer attributes, and timeline events into reporting dimensions.
- Configure AutoQA. QA leaders define criteria for accuracy, empathy, resolution quality, documentation, policy adherence, and escalation judgment.
- Configure VoC. CX leaders define themes such as delivery friction, product issues, billing confusion, account access, policy complaints, or churn language.
- Calibrate sample conversations. Supervisors review examples to tune evidence, thresholds, exceptions, and human-review routing.
- Route QA Insights. Send high-risk conversations, coaching needs, and customer friction patterns to the right owners.
What VoC Looks Like Once Connected
Once connected, Oversai turns Gladly conversations into a continuous Voice of Customer source.
Because Gladly centers the customer record, VoC can go beyond isolated messages. Oversai can identify when customers repeat the same issue, when a handoff creates frustration, when a policy causes unnecessary effort, or when unresolved history explains a negative conversation.
A CX leader might see that high-value customers are contacting support repeatedly about the same delivery promise. A product leader might see a recurring defect before returns data catches up. An operations leader might see that one workflow creates avoidable escalation.
That is the value of VoC from Gladly: customer language stays connected to customer context.
What AutoQA Looks Like Once Connected
AutoQA evaluates Gladly interactions against the standards your support team defines.
Oversai can score whether the agent understood the full customer context, acknowledged prior history, gave accurate information, followed policy, resolved the issue, documented the next step, and escalated at the right moment.
A reply can sound clear but still fail QA if it ignores a previous contact, repeats a step the customer already tried, or misses an account detail that should change the answer.
Oversai surfaces evidence so reviewers can focus on calibration, coaching, and exceptions instead of searching for examples.
What QA Insights Look Like Once Connected
QA Insights connect Gladly quality results to operational context.
Supervisors can compare quality by inbox, team, channel, topic, issue type, customer segment, or workflow. CX leaders can see whether negative sentiment clusters around repeat contacts, handoffs, timeline events, or specific customer attributes.
Examples:
- Repeat-contact customers receive technically correct but low-empathy replies
- VIP customers wait too long for escalation on urgent issues
- A return policy creates unnecessary contacts across multiple channels
- One inbox has strong resolution but weak documentation quality
- A product issue appears first as repeated customer timeline activity
These patterns help QA, CX, product, and operations teams work from shared evidence.
Example Use Cases
Gladly teams often use Oversai to:
- Score more customer conversations than manual QA sampling can cover
- Detect repeat-contact drivers from customer history and conversation text
- Audit escalations, complaints, refunds, or policy-sensitive interactions
- Find coaching opportunities tied to empathy, accuracy, and handoff quality
- Surface VoC themes for product, logistics, retention, and operations teams
- Prioritize human review by risk, sentiment, customer value, and issue type
For adjacent program design, read AutoQA scorecard criteria for CX teams and Best VoC tools for customer support. For commercial planning, see Oversai pricing.
Bottom Line
Gladly gives service teams a customer-centered workspace. Oversai helps leaders measure the quality, customer signal, and operational risk inside it.
Together, Gladly and Oversai help teams move from sampled QA and delayed feedback to continuous AutoQA, VoC analysis, and QA Insights across the customer relationship.
Frequently Asked Questions
Does Oversai replace Gladly?
No. Oversai works above Gladly as an AutoQA, VoC, and QA Insights layer. Agents can continue using Gladly while leaders analyze support quality and customer feedback in Oversai.
What Gladly data is useful for VoC?
Conversation content, customer profile attributes, channels, inboxes, topics, status, timestamps, handoffs, and selected timeline events can help classify themes, sentiment, effort, and root causes.
Can Oversai evaluate customer-history context?
Yes. Oversai can help evaluate whether an interaction accounts for prior contacts, unresolved issues, customer attributes, escalation history, and the full context needed for a good answer.