Contact centers spend millions deploying AI routing and coaching models, yet customer satisfaction scores remain flat. The bottleneck is rarely the decision algorithm. It's the broken transcription layer feeding it.
Most product teams building Decision Intelligence concentrate their engineering investment on the LLM, the routing rules engine, or the coaching scorecard UI. These components matter, but each sits downstream of the actual problem. If your audio capture layer produces high word error rates on accented, multilingual conversations, every intent classification, sentiment score, and routing decision the DI system produces relies on corrupted input. The model runs fine. The data doesn't.
Defining Decision Intelligence for contact centers
Decision Intelligence is a discipline that improves decision making by explicitly modeling how decisions are made and tracking whether outcomes improve over time. In practice, a DI platform combines decision modeling, AI, analytics, and operational rules to support, augment, or automate decisions, and closes the loop by measuring what happened.
The distinction from Business Intelligence and general AI matters for how you architect the system:
| Concept |
Primary function |
Output |
Example in a contact center |
| Business Intelligence |
Descriptive, retrospective |
Dashboards showing historical trends |
Historical call volume and resolution time reports |
| Artificial Intelligence |
Pattern recognition, prediction |
Predictions, classifications |
Detecting frustration on a call |
| Decision Intelligence |
Predictive and prescriptive |
Automated, governed action |
Routing a call based on detected sentiment and intent |
BI tells you what happened. AI tells you what a signal means. DI decides what to do about it and tracks whether the outcome improved.
Driving consistent contact center decisions
Intuition-based decisions scale poorly. When a supervisor manually reviews calls and coaches agents based on gut feel, quality varies by shift, by supervisor, and by the call that happened to get flagged. DI replaces this with a governed decision layer that processes every call through the same rules, makes every output measurable, and traces every improvement to a specific change in the model or the rule set. The result isn't just better average performance. It's a tighter distribution around that average, which is what CX consistency actually means.
Automating contact center workflows
Contact center DI workflows typically include post-call analytics, real-time routing, and agent prompts. Each workflow depends on the same input: a clean, structured transcript that accurately represents what the customer said, in whatever language they said it.
Strategic routing decisions, not guesswork
Static IVR trees route based on what a customer selects from a menu. DI routes based on what they actually mean. A DI routing engine evaluates real-time intent classification ("account cancellation" versus "billing inquiry"), live sentiment signals, CRM-sourced customer LTV, and churn risk scores, then applies a decision rule: if intent equals churn and LTV exceeds a threshold and sentiment reads as frustrated, route to a senior retention specialist with full context pre-loaded on screen. That's the difference between a menu-driven handoff and a governed routing decision.
Root causes of service quality gaps
Agent variance across shifts and skills
Experience, training recency, and shift timing produce measurable outcome variation across agents. Without governed decision logic, human judgment introduces noise at every touchpoint, and that noise compounds across thousands of calls per week.
Incomplete customer history
An agent without full call history treats a second complaint as a first contact, missing the context that would change their approach, offer, or escalation threshold. DI resolves this by surfacing structured conversation data from previous interactions at the moment it's relevant, but only if that historical data was captured and transcribed accurately in the first place.
Gut-feel routing and escalation decisions
Manual escalation decisions depend on whoever is supervising that hour. A DI escalation trigger based on sentiment plus intent plus CRM data fires consistently, at any hour, for any agent, with no dependency on supervisor judgment. The rule is the same on a Tuesday afternoon and a Saturday night.
Channel data silos impacting CX
When voice data lives in one system, chat in another, and email in a third, the DI layer never sees a complete customer picture. Fragmented infrastructure is one of the most common reasons DI deployments underperform. The model is technically capable, but it's making decisions on partial information.
Building the DI loop for consistent CX
A DI workflow has multiple stages, and a failure at the capture stage compounds through all downstream systems.
Accurately transcribe for DI readiness
Async batch transcription serves as a foundational step for post-call analytics, QA scoring, and routing refinement. When a call ends, the transcription layer receives the full recording and processes the complete audio context before returning word-level timestamps, speaker labels, and structured data. This full-context processing is what separates async batch from real-time streaming for accuracy-critical workflows: the model has access to the complete recording. Gladia's async transcription recommended parameters cover configuration details for contact center pipelines.
Pinpoint customer intent and context
With a clean transcript, a downstream LLM or intent classifier identifies what the customer actually wanted. The downstream routing decision engine uses the extracted intent, 'dispute charge,' 'cancel service,' 'request upgrade,' as its primary input. Named entity recognition (NER) can extract structured fields from the transcript, which may integrate with CRM systems. The accuracy of these extracted fields depends entirely on whether the underlying transcript rendered them correctly.
Guide agents to best outcomes
DI doesn't remove human judgment from the contact center. It narrows the decision space so agents focus on what matters: the conversation, not the logistics. DI systems can surface prompts to the agent screen during or after the call. The reliability of these prompts depends on transcript accuracy. When the STT layer misinterprets customer input, the downstream prompts may not align with the actual customer need, which can reduce system adoption.
Refine AI to drive CX consistency
In well-designed DI systems, outcome data such as call resolution, escalation rate, customer satisfaction score, and churn events can inform adjustments to the decision rules. If the system consistently miscategorizes a particular accent group's complaints, the feedback mechanism can surface the error, provided the transcript layer is accurate enough to isolate the cause.
Driving predictable customer experience
Standardized routing across all agents
Every call processed through a DI routing layer receives the same decision logic regardless of which agent is available or which supervisor is on shift. The routing rules encode the best judgment of your top performers and apply it uniformly. This is how you move from a distribution of outcomes to a predictable band.
Real-time coaching prompts during calls
For live-assist workflows, Gladia supports real-time transcription at approximately 300ms final latency.
Automated escalation based on sentiment
Text-based sentiment, not vocal tone: Gladia's text-based sentiment analysis is derived from the transcript text, not from vocal tone or acoustic features. The model classifies what the customer said, not how their voice sounded. This distinction matters for architecture: a system routing on acoustic emotion detection requires a different model than one routing on transcript-derived sentiment. Gladia provides the latter, which is the appropriate layer for most DI escalation workflows.
Why Decision Intelligence is only as good as the capture layer
Transcription errors degrade DI outcomes
Transcription errors can invert meaning and corrupt downstream decisions. Consider this example from a contact center interaction:
The customer says: "My renewal was not processed correctly."
A transcription with high WER returns: "My renewal was processed correctly."
The downstream impact cascades immediately: sentiment analysis may misclassify the statement, intent classification may misroute the call away from the appropriate support queue, and automated summaries may record incorrect information. By the time anyone notices, the churn event has already happened and the CRM record is factually wrong. This is what WER errors cost in a DI context, not transcription quality in isolation, but compounded decision failures across every downstream system.
Non-English intent detection flaws
Most STT APIs were built and tested on clean, American English audio. Performance degrades measurably on accented speech, regional dialects, and non-Latin languages. For CCaaS platforms serving Business Process Outsourcing (BPO) operations in Southeast Asia, South Asia, or Latin America, this represents a significant portion of call volume. When language detection fails mid-conversation, the system may deliver inaccurate transcripts that the routing engine treats as reliable input.
WER errors corrupt sentiment insights
In noisy, conversational, or multi-speaker environments, standard STT deployments can show elevated WER, above any DI production threshold. Gladia's published async benchmark puts Solaria-1 at on average 29% lower WER and 3x lower DER than alternatives on conversational speech. A significant gap in WER represents a meaningful difference in how reliably a DI system can route calls and surface accurate insights.
Code-switching: a DI data challenge
Code-switching is when speakers alternate between two or more languages within a single conversation. It's common in global contact centers, particularly for bilingual speakers in multilingual markets. A customer calling from the Philippines might open in English and shift to Tagalog mid-sentence when explaining a complex issue. Standard transcription models either fail silently, returning garbled output for the second language, or produce a session error that drops the utterance entirely. Gladia's code-switching detection identifies mid-conversation language changes across all 100+ supported languages.
Gladia: the core of your Decision AI stack
Feeding DI with foundational data
Gladia's API covers the full audio pipeline: transcribing audio and returning structured, LLM-ready output that includes word-level timestamps, speaker labels, detected entities, text-based sentiment, summaries, and translation across 100+ languages. Named entity recognition (NER) extracts structured information directly from the transcript. PII redaction is optional and must be explicitly configured. When enabled, it replaces sensitive fields [NAME][PHONE_NUMBER]with markers like [NAME] or [PHONE_NUMBER] before data enters any downstream system.
This structured output is what feeds a DI stack. Instead of piping raw audio to an LLM and hoping the model extracts the right fields, the pipeline receives clean, labeled data from the capture layer, ready to route to any model or rules engine, whether you use an integrated option or bring your own.
Async transcription accuracy for noisy, multi-speaker audio
Async batch processing is well-suited for noisy, multi-speaker contact center audio in post-call analytics workflows because the model processes the full recording context before returning the transcript. Speaker diarization attributes each utterance to the correct speaker, giving DI systems clean per-agent and per-customer signal.
According to Aircall's case study, the platform processes more than 1 million calls per week through Gladia's API and cut transcription time by 95%, from 30 minutes down to 1.5 minutes per call, after switching to Gladia. That throughput feeds searchability across calls, AI-generated summaries, sentiment scoring, agent coaching, and CRM webhooks, all from a single integration point.
Multilingual consistency at scale
Solaria-1 covers 100+ languages, including 42 languages that are not commonly supported by other API-level STT providers, Tagalog, Bengali, Punjabi, Tamil, Urdu, Persian, Marathi, and others that matter specifically for CCaaS platforms running BPO operations in Southeast Asia and South Asia. Gladia's async benchmark evaluates Solaria-1 against multiple providers across conversational speech datasets.
Pricing is public and per-hour based on audio duration. The Starter plan runs USD $0.61/hr for async transcription. Higher-tier plans offer volume discounts.
For EU-based CCaaS platforms, Gladia is headquartered in Paris. Gladia holds SOC 2 Type II, ISO 27001, HIPAA, GDPR, and PCI certifications. For CCaaS teams evaluating the end-to-end pipeline, the CCaaS use case page outlines how the architecture maps to specific contact center workflows.
Start with 10 free hours and test Solaria-1 on your own audio before committing to a plan.
FAQs
How is Decision Intelligence different from Business Intelligence in a contact center?
BI visualizes historical data on dashboards and requires a human to interpret it and decide what to do. DI automates the decision itself, firing a governed action, route, escalate, or coach, based on predefined rules applied to real-time or post-call data.
Why does WER matter for a production DI system?
Production conversational AI systems benefit from low WER on conversational speech, with compliance-critical workflows requiring particularly tight accuracy. Model selection for the capture layer directly determines DI reliability.
How long does integrating a transcription API for a DI stack actually take?
Multiple Gladia customers report sub-24-hour integration to production using the REST API or WebSocket connection. Lightweight Python and JavaScript SDKs are available, and Gladia's documentation covers authentication, parameter configuration, and audio intelligence feature activation in a single reference.
Does Gladia's sentiment analysis detect vocal tone or acoustic emotion?
No. Gladia's sentiment analysis is derived from the transcript text using NLP models, classifying what the customer said rather than how their voice sounded.
Is PII redaction automatically applied to all transcripts?
No. PII redaction is an optional feature that can be configured in the API request.
Key terms glossary
Word Error Rate (WER): A standard metric for evaluating transcription accuracy that compares the transcript output to a reference text. Lower WER indicates fewer recognition errors, which is critical for any downstream system that acts on transcript data.
Diarization Error Rate (DER): A metric for evaluating speaker diarization accuracy. Lower DER indicates more reliable speaker attribution, which matters for contact center analytics that depend on separating agent speech from customer speech.
Code-switching: When speakers alternate between two or more languages within a single conversation or utterance. Standard STT models typically fail on code-switched audio, dropping or garbling the second language and producing incomplete transcripts.
Async transcription: A transcription workflow where audio is submitted after recording completes and the full file is processed before the transcript is returned. Async processing can offer advantages for accuracy-critical workflows where the model benefits from access to the complete recording before generating output.
