Most contact centers manually review only a small fraction of their calls, leaving the vast majority of agent interactions unscored and coaching decisions based on incomplete data.
To build a reliable automated call scoring system, you need audio infrastructure that captures 100% of calls, accurately transcribes multilingual and accented speech, and delivers structured data directly to your evaluation models. This guide breaks down the best practices for implementing AI-powered call scoring, from defining scorecard criteria to optimizing the underlying speech infrastructure for predictable unit economics.
Key elements of automated call scoring
Automated call scoring uses AI to evaluate agent-customer interactions against pre-defined criteria without manual listening. Instead of supervisors sampling a handful of calls per week, the system scores every call against the same criteria on the same timeline.
Manual vs. automated QA scoring
The coverage gap between the two approaches is the core argument for automation.
| Method |
Call coverage |
Scoring consistency |
Time to insight |
| Manual review |
1-2% |
Variable (reviewer fatigue, inconsistency) |
Days to weeks |
| Automated AI scoring |
100% |
Consistent (criteria-dependent) |
Minutes post-call |
Automated scoring removes the lottery element from QA. Every agent interaction gets evaluated, not just the calls a supervisor happened to pull. Note that consistent criteria don't eliminate scoring errors. They make errors systematic and auditable, which is why calibration and human validation remain essential (covered in the best practices section below).
Automating call scoring via speech AI
The pipeline runs in four stages after a call ends:
- Ingest: Your telephony system captures the call recording via native recording or a third-party integration.
- Transcribe: Your pipeline sends the audio file to an async STT API, which returns structured JSON with word-level timestamps, speaker labels, translated text, sentiment scores, and named entities.
- Evaluate: The transcript feeds into an LLM or rule-based scoring model that checks for keyword presence, compliance phrases, talk-to-listen ratios, and sentiment thresholds.
- Score and route: The system populates the QA scorecard, flags calls below a threshold for human review, and routes coaching alerts to the relevant supervisor.
Elements of an automated call scorecard
A CCaaS QA scorecard tracks criteria across three categories, each weighted to reflect compliance and business impact:
- Compliance and risk (15-40%): Required disclosure statements, authentication steps, regulatory script adherence. Compliance criteria carry high weight because a single violation can trigger legal or regulatory consequences. Exact weighting varies by industry and regulatory requirements.
- Resolution and accuracy (30-40%): First-call resolution, solution correctness, escalation handling.
- Communication and experience (20-30%): Empathy indicators, clarity, customer sentiment at call close.
Each criterion maps to a specific, AI-evaluable signal in the transcript: a keyword detected, a phrase matched, a sentiment score below a threshold, or a speaker turn indicating an unresolved issue.
Gladia's named entity recognition can detect entities like names, phone numbers, and account details when enabled. PII redaction, which replaces sensitive data with tokens like [NAME] or [PHONE_NUMBER], is an optional feature that requires explicit configuration and is not enabled by default.
Ensure multilingual call quality at scale
Global BPOs and contact centers serving diverse customer bases face a compounding problem: the QA challenge isn't just volume, it's language diversity. A QA system that works on American English and fails on Tamil or Tagalog doesn't scale.
AI scoring covers 100% of call volume and applies the same scorecard criteria to every call regardless of language, time of day, or call volume. In principle, an agent in Manila gets scored on the same compliance checklist as an agent in Mexico City. That consistency matters both for fairness in performance management and for the statistical reliability of aggregate data you use to drive coaching decisions. Post-call scores reach supervisors within minutes: Aircall cut transcription time by 95% (from 30 minutes to 1.5 minutes per call) and now processes over 1 million calls per week with AI-generated summaries and coaching alerts routed automatically.
Human error in call scoring at scale
Manual reviewers produce two failure modes at high volume. Fatigue-driven inconsistency means a reviewer scoring 50 calls on a Friday afternoon applies different standards than on a Tuesday morning. Language gaps mean QA coverage is structurally limited to languages your supervisors speak, which excludes large portions of a global contact center's call volume. Both failure modes produce agents who don't receive consistent, actionable feedback, and quality data that can't be aggregated reliably across regions.
Predictable QA costs for scaling
Add-on pricing is the hidden cost that breaks QA cost models at scale. AssemblyAI charges separately for diarization, entity detection, topic detection, and summarization, pushing effective rates to approximately $0.43–$0.45/hr before translation. Gladia's Starter and Growth plans include diarization, sentiment analysis, translation, and named entity recognition in the base rate, at $0.20–$0.61/hr depending on plan and volume. At QA scale, that difference compounds significantly. See the ROI model below for cost projections at 10,000 hours per month.
Building a scalable call scoring system
1. Design your QA scorecard criteria
Before writing a single API call, define your evaluation criteria in terms that map directly to transcript signals. Use this checklist as your starting framework:
QA scorecard design checklist:
- List all required compliance disclosures (word-for-word phrases the agent must state)
- Define resolution criteria (what a correctly resolved call looks like in transcript form)
- Set sentiment thresholds (e.g., flag calls where customer sentiment drops sharply in the final third)
- Identify key entities to track (product names, account numbers, regulatory terms)
- Define escalation signals (phrases like "speak to a manager" or "cancel my account")
- Set talk-to-listen ratio targets by call type (outbound sales vs. inbound support)
- Decide which criteria require exact-match keyword detection vs. LLM semantic evaluation
- Assign weights to produce a composite score
Criteria that work best for AI scoring are binary and verifiable in text: the agent either stated the required disclosure or didn't. Criteria requiring judgment on emotional nuance (like whether the agent "sounded empathetic") belong in the human validation layer, not the automated scoring model.
2. Evaluate STT engine performance
Transcription accuracy sets the ceiling for QA quality, and WER on your specific audio is the metric that matters. A missed phrase in the transcript is an invisible failure: the scoring model can't evaluate a disclosure it never received.
Solaria-1, Gladia's current production model, achieves on average 29% lower WER on conversational speech compared to alternatives, evaluated across 8 providers, 7 datasets, and 74 hours of audio with an open, reproducible methodology.
Test your STT provider against your actual call audio: noisy environments, multiple speakers, and the specific languages and accents your agents and customers use. Benchmark results on clean English audio don't predict performance on a contact center floor in the Philippines or India.
3. Configure automated scoring triggers
Automation rules determine which calls get scored, by which scorecard, under which conditions. Standard trigger parameters include:
- Call direction: Inbound vs. outbound (different scorecards typically apply)
- Duration: Exclude very short calls (typically under 10-15 seconds) to filter abandoned calls
- Phone number or queue: Route specific queues to specialized scorecards
- Tags or metadata: Apply scorecards based on CRM tags (e.g., "new customer," "escalation")
- Language detected: Route multilingual calls to language-appropriate scoring criteria
Define your highest-priority compliance scoring rules first, then layer performance scoring rules below. The first matching rule applies, so prioritize your highest-risk compliance criteria at the top of your rule stack.
4. Integrate automated scoring into current QA workflows
Selectra implemented this integration pattern to automate quality monitoring of sales calls. By feeding call transcripts to an LLM to determine the degree of compliance and extract insights, Selectra's QA team shifted from manually reviewing calls to validating the model's findings, a structural change that multiplied the team's output without adding headcount. In this workflow, AI scores every call and human reviewers focus on calls where the score and context require interpretation.
5. Coach for performance with AI scores
Aggregate scores over time to identify individual and team-level patterns. An agent who consistently misses compliance disclosures on longer calls needs different coaching than one scoring low on first-call resolution. Build a monthly coaching cycle: pull the bottom performers on each scorecard dimension, identify the specific recurring failure, and design a targeted intervention rather than a generic performance improvement plan.
Best practices for AI-powered call scoring
Hybrid QA for automated call scoring
Automated scoring handles 100% coverage of objective, binary criteria. Human QA handles complex edge cases and subjective criteria. In practice, this means human reviewers focus on calls where the AI score and customer feedback diverge, or where compliance interpretation is ambiguous. This shifts human effort from mechanical listening to strategic coaching and edge-case validation.
Async vs. real-time transcription for QA
Most QA scoring is post-call, which makes async (batch) transcription the right architecture choice. Async processing analyzes the full recording before producing the final output, improving accuracy, speaker attribution, and multilingual consistency. For live-assist use cases where supervisors monitor calls in progress, Gladia's real-time transcription supports approximately 300ms final transcript latency, but that is a separate architectural decision from post-call scoring. The recommended parameters documentation covers specific configuration options for post-call QA pipelines.
Use diarization for speaker ID
If your scoring model can't distinguish the agent from the customer, every compliance check and sentiment analysis is unreliable. Speaker diarization is non-negotiable for call center QA. Gladia's diarization is powered by pyannoteAI's Precision-2 model in async workflows and achieves on average 3x lower DER than alternative providers. It's included in the base rate on Starter and Growth plans with no add-on charge.
Calibrate call scoring models
Build a monthly calibration cycle into your QA operations:
- Review calls where AI and human scores diverge significantly (for example, by more than 20 points).
- Update custom vocabulary lists with new product names and regulatory terms.
- Retune LLM prompts based on false positive and false negative patterns in compliance scoring.
- Re-evaluate language model performance if you've expanded into new markets.
Pinpointing agent coaching needs with AI
Identifying agent skill gaps from calls
Automated scoring produces a searchable, filterable record of every agent interaction. Instead of a supervisor guessing at what a low-performing agent needs, the scorecard data identifies the exact recurring failure: consistently missing a required compliance disclosure, or failing to confirm account details before proceeding. That specificity makes the coaching intervention concrete rather than generic.
Flagging agent sentiment for coaching
Gladia's sentiment analysis derives sentiment from transcript language, not from vocal tone or acoustic characteristics. A system that transcribes speech and runs an NLP classifier is not performing acoustic emotion detection, which analyzes raw audio waveforms.
For coaching purposes, text-based sentiment is highly actionable. Calls where customer language turns progressively negative in the final third often indicate unresolved friction, regardless of vocal delivery.
"Gladia provides a highly accurate real-time speech-to-text solution for high volumes of support and service calls. Latency is low and accuracy high, even for numericals. We've appreciated the quality of support across pre-processing, post-processing, and model optimization." - Verified user review on G2
While this customer uses Gladia's real-time API for live workflows, async transcription powered by Solaria-1 typically delivers higher accuracy due to full-audio context.
Benchmarking agent performance for coaching
Aggregate scorecard data across your agent population to build ranked performance distributions. The top performers on each dimension can become your "Gold Standard": their call patterns and language inform updated scoring templates and serve as concrete training material for lower performers.
Actionable QA scorecard insights
Raw scores don't drive behavior change. The coaching action does. Map each scorecard criterion to a specific intervention with measurable success criteria. Agents consistently missing compliance disclosures need a targeted script review session and shadowed calls with a top performer, not a generic performance improvement plan. Specificity converts QA data into measurable behavior change.
Track automated QA's business impact
Agent metrics for AI QA impact
The two metrics most commonly tracked after implementing automated scoring are Average Handle Time (AHT) and First-Call Resolution (FCR). AHT may decrease as agents receive consistent, specific feedback on resolution paths rather than vague coaching. FCR may improve as compliance and resolution criteria become explicit and measurable. Track both metrics monthly to validate that your scoring criteria connect to actual performance outcomes.
Correlating QA scores with CSAT
QA scores and CSAT measure different things, a team can hit every internal compliance checkbox while customer satisfaction stays flat if those criteria don't map to what customers actually care about. If your QA model emphasizes criteria customers don't actually care about, scores can rise while satisfaction stays flat. Use CSAT trends to audit your scoring criteria periodically: when QA scores improve but CSAT doesn't, your scorecard is measuring compliance to internal standards rather than drivers of customer experience.
Building the ROI model for AI QA
Here's a realistic cost model at QA scale, using Gladia's public pricing, compared against a provider billing features separately:
| Volume |
Gladia Growth ($0.20/hr all-in) |
Base + add-ons at ~$0.43/hr* |
| 1,000 hrs/month |
$200 |
$430 |
| 10,000 hrs/month |
$2,000 |
$4,300 |
Based on AssemblyAI's published rates: $0.15/hr base transcription + $0.02/hr diarization + $0.08/hr entity detection + $0.15/hr topic detection + $0.03/hr summarization = $0.43/hr. Excludes translation costs.
At 10,000 hours per month, the pricing difference alone funds significant additional QA engineering or headcount capacity. The Attention x Gladia case study covers how conversation intelligence platforms use this infrastructure pattern to power CRM population and coaching scorecards at scale.
Scaling AI call scoring in production
Optimizing AI call scoring costs
The build-vs-buy decision for QA infrastructure comes down to total cost of ownership. Self-hosting an open-source model introduces DevOps overhead, scaling complexity, and WER exceeding 10% on real-world call audio, based on customer-reported production results. For context, production QA systems typically target WER below 5% for reliable compliance scoring. Teams switching to a managed API with all-inclusive pricing can save on DevOps effort and immediately gain diarization, translation, and sentiment without additional development work.
Data governance for production QA
Call center audio is sensitive data. Your STT provider's data handling policy is a compliance question.
Data usage and training policy: On Gladia's Growth and Enterprise plans, customer audio is never used for model training. No opt-out action required. On the Starter plan, data is used for training by default. This distinction is critical for compliance-sensitive organizations.
Gladia is SOC 2 Type II, ISO 27001, HIPAA, and GDPR-compliant, with EU and US data residency configured separately.
"It's based in EU so it fits our GDPR compliance requirements... The team is very reactive and helpful... The product works great." - Robin L. on G2
Contact center integration architecture
Gladia's API connects via REST for async transcription and WebSocket for real-time, with lightweight Python and JavaScript SDKs and native integrations for Twilio, Vonage, and Telnyx. Scoreplay reported integrating Gladia and releasing a production speech-to-text engine in less than a day of dev work using the same async transcription infrastructure pattern.
Implementation essentials for QA scorecards
Defining call scoring accuracy
Your QA score accuracy is bounded by your transcription accuracy. A scorecard criterion requiring detection of a specific compliance phrase produces a false negative every time the STT engine misses that phrase. For example, if your STT engine has 5% WER and a compliance disclosure is 20 words long, the cumulative probability of at least one transcription error in that phrase becomes meaningful, which can cause a false negative if your keyword matching is strict. This is why testing your STT provider on your actual call audio is non-negotiable before committing to a production QA system.
AI call scoring integration timeline
A basic async QA pipeline takes under 24 hours from API key to first scored transcripts. A production-grade pipeline with LLM evaluation, diarization, and CRM webhooks typically takes longer depending on your stack and evaluation logic, but Scoreplay's experience is representative of the baseline: "In less than a day of dev work we were able to release a state-of-the-art speech-to-text engine."
AI for accents, dialects, and code-switching
True mid-conversation code-switching breaks most ASR models at the transition point. The model either assigns the wrong language label or returns garbled output, corrupting everything downstream. This is a structural problem for contact centers serving Southeast Asia, South Asia, or Latin America, where bilingual and multilingual calls are common.
Solaria-1 detects language changes automatically without requiring a session reset, in both real-time and async modes, across 100+ supported languages. This includes 42 languages not covered by any other STT API, including Tagalog, Bengali, Punjabi, Tamil, Urdu, Persian, and Marathi. For contact centers processing calls across these language families, lower WER on compliance disclosures can be the difference between a reliable QA system and one that produces inconsistent scores at scale.
Start with 10 free hours to test Gladia on your own multilingual audio and see how it handles language detection, accent-heavy speech, and code-switching, then compare the transcript output against your current provider before committing to a production integration.
FAQs
How much does automated call scoring infrastructure cost?
Gladia's async transcription costs between $0.20 and $0.61 per hour of audio depending on plan, with diarization, sentiment analysis, translation, and named entity recognition included in the base rate on paid plans. At 10,000 hours per month on the Growth plan, that's $2,000 total with no add-on fees.
Can AI accurately score calls with heavy accents?
Solaria-1 achieves on average 29% lower WER on conversational speech compared to alternatives, benchmarked across 8 providers, 7 datasets, and 74 hours of audio. It supports 100+ languages including 42 not covered by any other STT API, with native mid-conversation code-switching detection across all supported languages.
Is customer call data used to train AI models?
On Gladia's Growth and Enterprise plans, customer audio is never used for model training. No opt-out action required. On the Starter plan, data is used for model training by default.
What's the difference between async and real-time transcription for QA?
Async transcription processes the full recording before producing output, which improves accuracy, speaker diarization quality, and multilingual consistency, making it the right choice for post-call QA scoring. Real-time transcription (~300ms latency) suits live-assist use cases where supervisor intervention during a call is required.
Key terms glossary
Word Error Rate (WER): The standard metric for measuring STT accuracy, calculated as substitutions + deletions + insertions divided by total words in the reference transcript. A lower WER means fewer transcription errors and more reliable downstream QA scoring.
Diarization Error Rate (DER): The metric measuring how accurately a system identifies who spoke when in a multi-speaker recording, combining speaker assignment error, missed speech, and false alarm speech. Poor DER means agent and customer utterances can get mixed up in the transcript, making per-speaker scoring unreliable.
Code-switching: The practice of alternating between two or more languages within a single conversation or utterance. Standard ASR models fail at language transition points, producing errors in segments immediately before and after a language switch.
Async transcription: Batch processing of a complete, pre-recorded audio file by a speech-to-text API. The model uses full-audio context for higher accuracy, better diarization, and typically more consistent multilingual handling compared to real-time streaming, making it the standard architecture for post-call QA workflows.
