In a 12-week pilot, HarborBite Restaurants grew quality evaluation from 5% to 85% of eligible calls and cut reviewer time per evaluation by 36%, without adding to its manual review workload.
At a Glance
| Client | HarborBite Restaurants (Name Annonymized) |
| Industry | Food & Beverage, Quick-Service Restaurants |
| Region | North America |
| Business profile | Regional restaurant operator with 80 locations |
| Contact center | 45 customer-support agents |
| Recorded voice interactions | About 12,000 per month |
| Solution | AI Quality Control (QC) |
| Pilot duration | 12 weeks |
| Primary use cases | Automated quality evaluation, campaign-specific scorecards, policy checks, reviewer validation, coaching insights |
| Implementation model | Commplify platform configuration with integration support and client-led QA governance |
Business Impact
The pilot produced four headline results:
- 5% to 85% evaluation coverage. Successfully evaluated calls rose from 5% to 85% of eligible recorded interactions.
- 36% less review time. Average human review time for comparable evaluations fell from 7.0 to 4.5 minutes.
- 91.5% criterion-level agreement. AI evaluation decisions matched independently established human references in a validation set.
- 12% to 9% omission rate. Missing order-confirmation steps fell in comparable human-audited samples after targeted coaching.
About HarborBite and the Challenge
HarborBite Restaurants is a quick-service restaurant group serving customers through dine-in locations, takeaway orders, and home delivery. Its regional support team handles order changes, delivery-status requests, missing-item complaints, cancellations, refunds, and escalations.
With 45 agents and about 12,000 recorded calls a month, the operation depends on consistent handling across agents and shifts. An established QA team samples interactions, reviews agent performance, coaches supervisors, and flags recurring process issues. Growing call volume was making broad visibility hard to maintain through manual evaluation alone.
Challenge 1: Limited Visibility
Analysts evaluated about 540 of roughly 10,800 eligible recorded calls each month, which is 5% coverage. Sampling was useful, but recurring mistakes across the wider operation stayed hidden.
Challenge 2: Inconsistent Standards
HarborBite had clear requirements for order confirmation, delivery expectations, policy communication, and complaint handling. Some criteria involved judgment, though, and reviewers sometimes read the same call differently. That made calibration important and slowed agreement on coaching priorities.
Challenge 3: Slow Identification of Recurring Issues
Reviewers spent most of their time listening to calls, locating the relevant moments, completing scorecards, and documenting findings. By the time trends were summarized, supervisors had often missed the chance to act.
Challenge 4: Findings that Didn’t Lead to Improvement
A single quality score rarely showed whether the cause was agent behavior, unclear policy, outdated information, or a system problem. The team needed a consistent way to route findings to the people who could fix them.
The Solution
HarborBite configured Commplify’s AI Quality Control to support its existing quality-assurance process, not to replace its agents, supervisors, or QA team. The pilot covered recorded voice interactions from the order-support and delivery-complaints queues and rested on four connected elements.
- Approved quality scorecards. The QA team defined criteria covering order-detail confirmation, delivery communication, required process steps, and clear next actions. Campaign-specific rules set when each criterion applies and which failures need extra scrutiny.
- AI-assisted conversation evaluation. Eligible recordings and their metadata run through the configured workflow. Commplify’s AI QC assesses call content against the agreed criteria and flags potential misses or inconsistencies.
- Evidence and human validation. Reviewers investigate uncertain, critical, or disputed findings. When a criterion depends on an external action, such as whether a delivery case was actually created, the process requires authorized business-system evidence instead of relying on the agent’s spoken statement.
- Quality insights for coaching and improvement. Validated findings are grouped into recurring issue categories and shared with the responsible supervisors and process owners. Managers stay accountable for coaching decisions, and operations teams handle policy, knowledge, or technical fixes.
What Commplify Delivered
A configurable evaluation framework. The pilot scorecard covered four priority criteria. HarborBite approved the final wording, applicability conditions, severity classifications, and review rules.
| Criterion | Evaluation objective |
| Order confirmation | Did the agent confirm applicable order or delivery details? |
| Delivery expectations | Did the agent communicate a clear and appropriate next step or timeframe? |
| Policy adherence | Did the conversation follow relevant approved policy requirements? |
| Case completion evidence | Is the required case or action supported by authorized system evidence? |
Recording and metadata integration. Commplify connected the agreed recording source and call metadata to the AI QC module. Customer identifiers, campaign labels, and interaction references follow HarborBite’s access and data policies. Where available and authorized, CRM or order-service records supply evidence for specific criteria.
Evaluation and exception workflows. Commplify structures its findings for review. Human reviewers focus on selected quality samples, uncertain decisions, serious potential failures, and issues that need external verification. The workflow separates a possible failure flagged by AI from a finding confirmed after the right checks.
Reporting and trend analysis. Reports cover successfully evaluated interactions, processing exclusions, criterion-level findings, reviewer agreement, and recurring issue categories, so the QA team can see patterns well beyond its earlier sample.
Implementation and validation support. The engagement included scorecard configuration, integration work, evaluation testing, QA team enablement, and a controlled release. HarborBite’s quality leaders approve the standards, accept the pilot, and act on the findings.
Sample Scenario: A Delivery Complaint Becomes Actionable Feedback
A customer calls HarborBite because a delivery is running late. The agent explains that a delivery case has been opened and promises an update within 30 minutes. The next step is clear, but the agent never gives the customer the case reference.
The AI QC evaluates the call against the approved criteria:
| Evaluation criterion | Finding |
| Follow-up timeframe communicated | Met |
| Case reference communicated | Not met |
| Case successfully created | Requires CRM verification |
| Appropriate escalation | Not applicable |
A quality reviewer examines the evidence and confirms the missing reference. A check of the authorized CRM record then establishes whether the case was actually created, and the verified finding is categorized for coaching or process improvement.
Instead of just logging a lower score, the team gets a specific behavior to reinforce: give the customer the case reference before closing the call.
This walkthrough uses synthetic dialogue (based on real data), not a recorded customer conversation.
The Results
Broader evaluation coverage
In a representative month, HarborBite recorded about 12,000 calls, and 10,800 met the agreed eligibility criteria. Before the pilot, reviewers manually evaluated 540 of them. After deployment, Commplify successfully processed 9,180 eligible calls in a comparable month, a 17-fold increase in coverage.
| Measure | Before | Pilot |
| Recorded calls per month | 12,000 | 12,000 |
| Eligible calls | 10,800 | 10,800 |
| Successfully evaluated calls | 540 | 9,180 |
| Evaluation coverage of eligible calls | 5% | 85% |
The remaining 1,620 eligible calls aren’t counted as evaluated. They need investigation or reprocessing because of source, recording-quality, transcription, or processing limits. Broader AI evaluation doesn’t mean 85% of calls got individual human review, or that every AI finding was correct.
Faster review of selected interactions
The team compared reviewer effort on matched sets of 500 calls of similar complexity. Average active review time dropped from 7.0 to 4.5 minutes per call, because reviewers started from structured findings instead of evaluating each call from scratch.
| Measure | Before | Pilot |
| Average human review time | 7.0 minutes | 4.5 minutes |
| Review time for 500 comparable calls | 58.3 hours | 37.5 hours |
| Time saved | n/a | 20.8 hours |
That is about 36% less review time within the selected review task. It isn’t a 36% cut in total QA staffing cost, since exception handling, calibration, technical administration, and program governance also take effort.
Reliability validated against human references
A separate holdout set of 400 calls was reviewed against an agreed human reference. With four criterion decisions per call, Commplify’s AI QC agreed with the human reference on 1,464 of 1,600 decisions, or 91.5%.
A deliberately enriched test subset held 50 human-confirmed serious failures. Commplify caught 47 and missed 3, a 94% detection rate within that set. Because the subset was enriched, the result doesn’t reflect how often critical failures occur in practice, and it doesn’t show that human review can be removed. The three misses are the reason validation, escalation, and ongoing quality control stay in the process.
Early evidence of service improvement
In human-audited samples of 500 calls each, missed order-confirmation steps fell from 60 calls (12%) at baseline to 45 calls (9%) in a later pilot sample. That is a 3-percentage-point drop, or a 25% relative decrease. Supervisors used the findings to deliver focused coaching and to clarify the order-confirmation checklist.
Technologies and Services
Technologies used
- Commplify for AI-assisted quality evaluation
- Recorded voice interaction ingestion and associated metadata
- Configurable campaign-specific scorecards
- Conversation transcription and language-processing components
- Authorized CRM or order-service integration for verification
- Quality reporting and analytics
- Human validation, calibration, and exception-review processes
Services provided
- Quality-management discovery and use-case scoping
- Scorecard design and configuration support
- Integration engineering
- Evaluation calibration and acceptance testing
- Pilot implementation and quality-team enablement
- Performance measurement and reporting design
Ongoing outsourced quality review, coaching administration, and fully managed quality operations are separate service commitments.
Why This Approach Worked
The gains didn’t come from replacing the quality team with AI. They came from five operating decisions:
- A defined starting scope. The pilot focused on common order-support and delivery interactions instead of trying to evaluate every service issue at once.
- Approved standards. HarborBite decided what good service looks like, including required checks and exceptions.
- Human validation. Reviewers kept a defined role in confirming uncertain or consequential findings and monitoring scoring reliability.
- Connected evidence. The process never treated a spoken promise as proof that an external action happened.
- Follow-through. Findings went to the right coaching, knowledge, process, or technical owners.
Together, these decisions show how AI quality intelligence can support a more structured and accountable quality-management process.
What’s Next
After the first deployment, HarborBite could extend the program to more customer-service workflows:
- Reviewing refund and cancellation conversations
- Adding restaurant-location or campaign-specific scorecards
- Extending evaluation to eligible chat and email interactions
- Improving access to verified order and case records
- Monitoring criterion-level reliability over time
- Using recurring quality patterns to prioritize process and knowledge improvements
Any expansion depends on validated pilot results, integration readiness, approved standards, and the organization’s operating priorities.
This page was last edited on 15 October 2026, at 12:42 am
