When customer service consistency breaks down, the root cause is rarely agent training. It is almost always a failure of your routing infrastructure to match the customer's real-time intent with the right agent expertise. Traditional Interactive Voice Response (IVR) systems typically triage calls using button presses and static decision trees, which can struggle when a caller speaks off-script, switches language, or describes a problem outside predefined categories.
Modern speech infrastructure transforms contact center operations by converting raw voice streams into structured, LLM-ready datasets. By deploying machine learning models that analyze intent, sentiment, and language mid-conversation, product teams eliminate response drift and ensure uniform service quality across thousands of concurrent calls. The speech infrastructure layer is not a supporting actor in this architecture: it is the foundation every downstream system depends on.
Building intelligent routing with speech infrastructure
In enterprise contact centers, an intelligent speech infrastructure layer connects raw audio inputs with systems of record, applying machine learning models to convert unstructured conversation into structured, actionable data. Where traditional business intelligence surfaces a signal, this infrastructure governs what happens next, uniting transcription, analytics, and workflow orchestration to transform insight into consistent, measurable action.
In a contact center context, this infrastructure layer processes raw telephony data from your CRM, ticketing platform, and telephony systems. It ingests audio streams, applies machine learning models to classify intent and sentiment, and provides structured outputs that enable routing decisions based on real-time conversation state rather than static rules.
This matters because errors at the transcription layer can propagate through downstream systems. LLMs, sentiment models, and CRM pipelines are only as reliable as the transcript they process. A customer name misheard or a product code garbled can corrupt records that read that entry.
Key elements of intelligent routing
Four components must operate within a unified pipeline to prevent data degradation:
- Real-time audio capture: Raw telephony audio streamed to the transcription layer with low buffering delay.
- High-accuracy transcription: A speech-to-text model that handles accented speech, noisy environments, and mid-conversation language changes.
- Intent classification: A machine learning model that parses the transcript and assigns intent labels, enabling dynamic routing decisions.
- CRM data integration: Structured outputs that can be pushed to the system of record so routing decisions can leave an auditable trace. When these components are split across multiple vendors, integration points can introduce opportunities for data degradation. Our Audio-to-LLM pipeline consolidates this stack, returning speaker-attributed transcripts, named entities, and sentiment scores in structured outputs.
AI versus legacy routing models
Traditional automatic call distributors route on simple rules: longest idle agent, highest priority queue, or static skill tags. AI routing uses predictive models that continuously learn from real outcomes to dynamically optimize customer-to-agent matching, as defined in Callzilla's contact center glossary.
| Dimension |
Rules-based routing |
ML-based routing |
| Input variables |
Static skill tags |
Multi-dimensional models analyzing conversation context |
| Routing latency |
Fast lookup |
Sub-second with live inference |
| Complex customer states |
Limited flexibility |
Adaptive model-based handling |
| Maintenance overhead |
Manual rule updates |
Model retraining on outcome data |
Consider a customer calling about a billing dispute in Tagalog, with a high churn score and three contacts in 30 days: that multi-variable state is one no static ruleset resolves correctly. A machine learning model matches that caller to the agent with the highest first-contact resolution rate on billing escalations for that language and customer cohort.
Reducing agent performance gaps
Intelligent speech infrastructure standardizes the information available to agents regardless of tier. A tier-1 agent handling an escalated call can see the same real-time intent classification, the same CRM context, and the same AI-generated response suggestions as a tier-3 specialist. The gap between your fastest and slowest resolution times narrows not because agents perform identically, but because the system provides every agent with the same structured context at call start.
Key data extraction of names, account numbers, and intents from calls, run automatically by our named entity recognition layer, meaning agents confirm rather than capture. That shift alone reduces handle time and substantially reduces the transcription-to-CRM hand-off errors that silently corrupt records.
How real-time intent logic curbs response drift
Response drift can occur when agents deviate from approved brand guidelines and compliance scripts, often because they lack real-time guidance calibrated to the specific conversation state. Real-time intent detection closes that gap by generating intent classifications from partial transcripts as the customer speaks, feeding classification models continuously throughout the conversation.
We provide low-latency partial transcripts through streaming audio processing, which can feed into downstream intent classification layers, as detailed in our latency and accuracy benchmarks guide. The agent receives a suggested response category tied to an intent label rather than a personal interpretation of what the customer said.
Reducing cross-lingual routing errors
Routing a bilingual customer to a monolingual agent creates immediate friction that typically extends handle time and reduces satisfaction. The cost of a misrouted call is recoverable but avoidable with automatic language detection.
Automatic language detection in our pipeline identifies the caller's language during the conversation. That language label feeds directly into the routing layer, matching the caller to a qualified agent.
Reducing manual triage with AI routing
Manual triage queues introduce two failure modes: inconsistency in how calls are categorized and bottlenecks during peak volume. Automated, intent-driven routing substantially reduces both. The routing decision applies the same model inference on every call, removing the variability that comes from supervisor judgment under pressure. Aircall cut transcription time by 95% (from 30 minutes to 1.5 minutes per call) and now processes over 1M calls per week through our API.
Handling code-switching and accents
Code-switching, alternating between two or more languages within a single conversation, is a standard communication pattern in multilingual contact centers. Many legacy STT systems struggle with this, either returning degraded output or locking onto the first detected language for the entire call, which can corrupt the downstream intent signal.
Our code-switching detection in Solaria-1 handles language changes across its full language breadth in both real-time and async modes, with no manual language configuration required. The model performs automatic language detection without a separate upstream language identification step. For contact centers serving business process outsourcing (BPO) hubs where callers regularly mix English with Tagalog, Bengali, or Tamil, this capability is essential for a functional routing pipeline.
For European contact center audio specifically, Solaria-3 maintains accuracy on noisy, accented speech across English, French, German, Spanish, and Italian. Solaria-3 is async-only and optimized for post-call analysis and batch workflows.
Reducing response drift with sentiment analytics
Sentiment analytics in contact centers means text-based sentiment inference, NLP models analyzing transcript words, not vocal characteristics from the audio waveform. Acoustic emotion detection, which analyzes pitch, tone, and prosodic features from the raw waveform, is a separate capability. Conflating the two can create compliance risk when emotional state claims appear in agent coaching reports.
Our sentiment analysis operates on the final transcript, making results reproducible, auditable, and directly tied to the words the customer actually used. Sentiment derived from text is consistent across agents reviewing the same call, which matters when coaching scores must hold up in a performance review.
"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
Using AI to flag high-risk interactions
Real-time sentiment tracking turns your transcription pipeline into an early-warning system for customer escalation. When sentiment scores in a call cross a negative threshold, the structured output triggers downstream workflows that alert supervisors.
{
"utterance": {
"speaker": "customer",
"words": "This is the third time I've called about this.",
"sentiment": "negative",
"sentiment_score": -0.82,
"entities": [{"type": "ORDINAL", "value": "third"}]
}
}
This output schema is directly consumable by a downstream escalation workflow without additional parsing or transformation.
AI-driven guardrails for agent responses
Sentiment data from the transcript triggers real-time prompts on the agent desktop when a call crosses a negative threshold, surfacing the next approved script branch based on historical resolution data for interactions with similar sentiment trajectories. The practical effect is that brand voice consistency can hold under pressure. An agent handling a frustrated customer on a high-volume afternoon can use the same approved language as a senior agent deliberating on the same scenario.
The call center voice analytics pipeline that powers this runs on the same transcript data used for post-call QA, so compliance teams can audit both the conversation and the system state that was active at the time.
Quantifying sentiment drift in conversations
Measuring sentiment delta, the difference between the sentiment score at call start and at resolution, gives product teams a service quality metric that does not rely on post-call survey completion rates. A call that starts at neutral and ends positive represents a successful de-escalation. A call that starts neutral and ends negative despite a confirmed resolution indicates a process gap, not an outcome gap. Correlating sentiment delta with agent, language, and call type turns that signal into actionable coaching material.
How intelligent routing eliminates outcome variance
The gap between receiving a customer signal and acting on it correctly is where routing decisions either compound or close service variance. An AI-driven decision framework analyzing conversation data in real time suggests optimal responses, resources, and routing paths to agents during live calls, compressing the time to the right action without replacing agent judgment.
Intelligent routing operates on multiple data streams simultaneously: historical CRM data (past interactions, purchase history, churn risk), real-time conversation signals (current intent label, sentiment score, detected language), and agent performance data (first-contact resolution rate by issue type, average handle time by language). The intersection of those streams produces routing recommendations quickly.
Matching agent expertise to interaction data
Generic tier-based routing assumes any tier-2 agent can handle any tier-2 call, which is rarely true in practice. ML-based matching updates continuously as agents handle more calls and surfaces performance differences that manual tagging misses.
Minimizing resolution time variance
The gap between your fastest and slowest resolution times on the same call type is a direct measure of routing and information asymmetry. Structured context at call start (caller history, current intent, sentiment trajectory, and the next approved action) is what narrows that gap, ensuring every agent arrives at the interaction with the same information regardless of tier. When Aircall integrated our STT infrastructure, they built searchability across call libraries, automated summaries, sentiment detection, agent coaching, and CRM enrichment as product capabilities rather than infrastructure projects.
Preventing repeat contacts for the same issue
Repeat contacts on the same unresolved issue represent costly outcomes in contact center operations. When the transcript layer identifies an open or recently closed issue in a caller's account history, the routing decision shifts automatically to a specialized path, before the caller re-explains their situation. The key data extraction layer pulls account numbers, issue types, and other entities from the transcript for downstream processing.
Deploying decision logic for stable performance
Deploying decision logic in production requires resolving four technical choices before integration begins: hosting model (cloud SaaS versus on-premises), latency budget, validation pipeline, and cost model at realistic scale. Gladia operates as a cloud-based service with EU and US data residency options; teams with strict data residency requirements can review the SOC 2 Type II, ISO 27001, and GDPR implications of each cloud hosting configuration before committing.
Latency budgets for real-time agent assistance
The critical latency budget for live agent assistance is around 300 milliseconds for the final transcript. Anything above that and the agent receives the routing suggestion after the conversational moment has passed. Our real-time transcription pipeline running on Solaria-1 delivers final transcripts in approximately 300ms, with partial transcripts returned in under 103ms to feed downstream intent classification during the conversation.
Validating AI accuracy in production
Automated QA pipelines for transcription accuracy run on every call rather than sampled subsets when infrastructure allows. The baseline metric is word error rate (WER) on your specific audio conditions: your telephony codec, your caller demographics, your language distribution. Our open benchmark methodology covers Solaria-1 across 8 providers and 74+ hours of audio and is fully reproducible, which means your team can run the same tests on your own recordings rather than trusting vendor-reported figures.
Multilingual model performance across regions
Contact center BPO hubs in South and Southeast Asia, Latin America, and North Africa require language coverage that most STT APIs cannot provide at production accuracy levels. Solaria-1 supports 100+ languages, including languages with limited coverage in competing APIs such as Tagalog, Tamil, Bengali, Punjabi, Urdu, Persian, and Marathi. That coverage is the difference between a routing pipeline that functions across your global footprint and one that defaults to English when agents in Manila or Chennai handle calls.
For European business audio, Solaria-3 reaches 6.4% WER on Earnings22 financial calls (the only model under 7%) and 33.9% on Switchboard conversational speech (the only model under 35%), per the Solaria-3 benchmark methodology.
Scaling without ballooning API costs
The unit economics failure mode for most contact center AI stacks is add-on fee stacking. Base transcription can look affordable, but stacking diarization, sentiment analysis, entity detection, and summarization as separate line items can significantly increase effective cost before volume discounts.
Our Growth plan bundles all audio intelligence features, including speaker diarization, translation, named entity recognition, sentiment analysis, and summarization, at the base per-hour rate with no add-on fees. At scale, the gap between a stacked add-on model and an all-inclusive rate can be substantial.
Monitoring output quality across workflows
Post-call analytics on batch-processed recordings provide higher-accuracy ground truth than real-time metrics for validating your decision intelligence framework. Async transcription can process the full recording with complete context, enabling high accuracy on diarization, multilingual handling, and entity extraction. Our speaker diarization, powered by pyannoteAI's Precision-2 model, is available in async workflows and attributes each word to a speaker label, which enables per-agent performance analytics.
Quantifying resolution gaps by agent
Correlating transcription data with CRM outcomes at the agent level produces coaching material tied to specific call types and languages rather than generic performance scores. Which agents have above-average first-contact resolution rates on billing disputes in Spanish? Which agents consistently receive negative sentiment scores in the final two minutes of long calls? Post-call transcription data answers both questions without requiring manual call review.
This is how multilingual customer support operations at scale maintain QA coverage across language cohorts. Contact center QA teams shift from manual call review to AI-assisted validation after integrating transcription pipelines.
Language-specific NPS performance tracking
Segmenting customer satisfaction scores by language and region is the correct diagnostic for a multilingual contact center, but only if the transcription layer that feeds those satisfaction models is accurate across all languages. If your NPS for French-speaking callers is consistently 8 points below your English baseline, the first diagnostic question is whether your STT model performs equivalently on French audio before examining agent or process variables.
Quantifying ROI on service stability
The financial case for intelligent speech infrastructure typically focuses on three metrics: reduced average handle time, lower agent churn from better tooling and coaching, and increased first-contact resolution rate. Each feeds directly into customer retention. The structured outputs our CCaaS infrastructure produces make all three measurable from the same data source: the transcript.
Addressing implementation risks and costs
The three objections we hear most often from product leaders evaluating this architecture are: how to define acceptable accuracy thresholds, how long integration actually takes, and what happens to customer audio after processing.
Defining production-grade accuracy thresholds
Acceptable WER and diarization error rate thresholds are specific to your use case, not to a universal standard. For a contact center where transcripts feed CRM population and compliance logs, higher WER on your primary language can produce entity errors that compromise data quality downstream. Contact center audio is typically noisier than some other audio types, which is precisely why Solaria-3's real-world business audio benchmarks on customer recordings matter more than clean read-speech benchmarks for this use case.
"Gladia AI impresses with its speed and transcription accuracy. The platform is extremely user-friendly... I've noticed that if the transcription is incorrect at first, it often takes just a few seconds for the system to correct itself and provide the right result." - Mohamed M. on G2
Estimated hours to go live
Many product teams are in production quickly. Our Python and JavaScript SDKs expose the full async and real-time pipeline through REST and WebSocket interfaces. Native integration with Twilio means telephony connections do not require custom middleware, and we provide implementation guides for other providers. For teams adding Amazon Connect transcription or Vonage call transcription, integration guides cover both configurations specifically. Direct Slack access to our engineers, not a ticket queue, accelerates the edge cases that block production deployments.
How we handle your call recording data
On our Growth and Enterprise plans, customer audio and transcripts are never used for model training. No opt-out action is required, and there is no enterprise contract clause to locate. Multi-region data residency across EU and US clusters ensures audio never crosses jurisdictional boundaries when configured for your compliance requirements. Our compliance hub documents our SOC 2 Type II, ISO 27001, GDPR, HIPAA, and PCI certifications.
Note that on the Starter plan, customer data can be used for model training by default. Teams handling sensitive contact center audio should be on Growth or Enterprise.
Which Gladia model works best for contact centers?
The decision between Solaria-1 and Solaria-3 maps directly to your audio conditions and language distribution:
- Solaria-3 is the correct choice for European business audio across English, French, German, Spanish, and Italian, including noisy call center recordings and fast-paced speech. It achieves 26% lower WER than Solaria-1 on real customer English recordings and ranks #1 against AssemblyAI, ElevenLabs, Deepgram, Mistral, and Speechmatics on production recordings. It is async only.
- Solaria-1 is the correct choice for maximum language breadth across the model's full language coverage, real-time streaming at under 103ms partial latency, and languages outside the core European set. If your BPO operations include Tagalog, Bengali, Tamil, or Urdu, Solaria-1 covers those routes at production quality.
Both models are complementary. Teams running European contact centers alongside global BPO operations can use Solaria-3 for European calls and Solaria-1 for all other languages from the same API.
Get started with Gladia and test both models on your own contact center recordings to measure the WER difference on your specific audio conditions before committing to a plan.
FAQs
Does Gladia support real-time speaker diarization?
No, speaker diarization is powered by pyannoteAI's Precision-2 model and is only available in asynchronous (batch) workflows. For real-time streaming, speaker attribution should be handled in post-processing for higher accuracy.
How does Gladia handle customer data privacy on paid plans?
On Growth and Enterprise plans, customer audio and transcripts are never used for model training by default and no opt-out action is required. The Starter plan can use customer data for training by default.
What is the latency of Gladia's real-time transcription?
Our real-time pipeline running on Solaria-1 achieves approximately 300ms final transcript latency, with partial transcripts returned in under 103ms to power low-latency downstream applications like next-best-action routing.
How do I choose between Solaria-1 and Solaria-3 for contact center audio?
Use Solaria-3 for European business audio in English, French, German, Spanish, and Italian. Use Solaria-1 for real-time streaming, code-switching across 100+ languages, and language coverage beyond the core European set.
What does all-inclusive pricing mean at contact center scale?
Most STT providers charge a base transcription rate and layer diarization, sentiment analysis, named entity recognition, and summarization as separate add-on fees. Deepgram, AssemblyAI, and Speechmatics all price at least some of these as add-ons, meaning the effective cost per hour runs materially higher than the headline rate once you enable the features a contact center pipeline actually requires. On our Growth plan, all of those features are bundled into the base per-hour rate with no add-on fees, as low as $0.20/hr async. At contact center scale, the difference compounds quickly. The full pricing breakdown covers all tiers with per-hour examples.
Key terms glossary
Word Error Rate (WER): The standard speech recognition accuracy metric, calculated by dividing the sum of insertions, deletions, and substitutions by the total number of words spoken. Lower is better.
Diarization Error Rate (DER): The metric for speaker diarization performance, measuring the percentage of call time attributed to the wrong speaker or missed entirely.
Code-switching: Alternating between two or more languages within a single conversation or sentence, a standard pattern in multilingual contact centers that breaks most legacy STT systems.
Next-best-action (NBA) routing: An AI-driven decision framework that can analyze real-time conversation data to suggest optimal responses, resources, or routing paths to agents during live calls.
System of Intelligence: An enterprise software layer that can sit between raw data sources like telephony streams and systems of record like CRMs, executing decision logic to transform raw input into structured, actionable outputs.
Interactive Voice Response (IVR): Automated phone systems that use button presses and static decision trees to route calls, often breaking down when callers speak off-script or describe problems outside predefined categories.
Business Process Outsourcing (BPO): Outsourcing of business functions to third-party providers, particularly common in contact center operations across South and Southeast Asia, Latin America, and North Africa.
Net Promoter Score (NPS): A customer satisfaction metric measuring likelihood to recommend a service, scaled from -100 to 100.
Contact Center as a Service (CCaaS): Cloud-based customer service software that provides contact center functionality without on-premises hardware.
