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Agentic AI in the contact center: autonomous agents and the STT layer
TL;DR: Autonomous contact center agents fail when their STT layer fails. Transcription errors do not stay contained to the transcript, and a misheard account number, a missed compliance phrase, or a wrong speaker attribution propagates into every downstream system that acts on it. For operations leads deploying agentic AI, the decisions that determine whether automation holds or collapses under production conditions are: which STT model fits which workflow, how accuracy requirements shift across deployment stages, and why STT selection is a compliance decision as much as a product one.
Adding real-time streaming transcription to an async STT pipeline: a build guide
TL;DR: Adding real-time transcription to an existing async pipeline does not require a rewrite. The production pattern is hybrid: stream audio to Solaria-1 via WebSocket for sub-103ms partials and approximately 300ms end-to-end final latency, while buffering the same audio for Solaria-3 async processing with full diarization and entity extraction. The engineering work is WebSocket lifecycle management, buffering, VAD (Voice Activity Detection) configuration for turn-taking, and deduplication logic. This guide covers each layer with code examples and latency budgets.
Voicebot for call centers: how speech-to-text powers automated phone agents
TL;DR: A voicebot is only as effective as its underlying speech-to-text layer. Two requirements determine whether an automated phone agent holds up at production scale: partial transcript latency within a 300ms total pipeline budget, and production-grade accuracy under real telephony conditions, such as noisy, accented, codec-compressed audio. When the STT layer is slow or inaccurate, every downstream system inherits the error: wrong transcripts corrupt CRM records and misroute callers. This playbook covers the latency budgets, accuracy thresholds, and cost models that determine whether a voicebot improves or erodes your operational metrics.
How Aircall cut transcription time by 95% with Gladia
Published on Oct 9, 2025
The contact center is transforming. Traditionally defined by manual workflows, siloed data, and reactive customer service, today's Contact Center as a Service (CCaaS) platforms are embracing a new era—one driven by real-time AI and automation.
Transcription lies at the core of this transformation. Converting voice to text with speed and precision unlocks a cascade of next-gen capabilities: automated summaries, sentiment detection, agent coaching, CRM enrichment, and more. But many legacy or in-house solutions fall short—too slow, too inaccurate, or too resource-heavy to scale.
Aircall, the leading AI-powered voice platform for growing businesses, recognized this inflection point early. To meet the growing demand for fast, intelligent insights from customer conversations, Aircall turned to Gladia’s speech-to-text API.
Here’s how Aircall reduced transcription time by 95%, empowered its users with near-instant insights, and laid the groundwork for a smarter, AI-driven CCaaS future.
About Aircall
Aircall is an integrated customer communications and intelligence platform. It unifies voice and digital channels into one seamless platform, offering one-click integrations with leading CRMs and over 250 business tools. With a strong focus on cloud-based voice solutions, Aircall helps teams streamline conversations, improve customer support, and drive sales efficiency.
Farid Issabhai, Staff Engineer at Aircall, is at the forefront of Aircall’s AI and transcription initiatives. He played a key role in integrating cutting-edge technologies, including Gladia’s speech-to-text API, into Aircall’s workflows.
Challenge: More accurate, fast, and scalable transcription for global telephony
As a leading voice platform, Aircall processes thousands of calls every day across diverse languages and use cases, from customer support to sales interactions. Initially, Aircall developed an in-house transcription engine, but maintaining and improving it proved challenging.
Solution: Gladia’s speech-to-text API
After evaluating different STT API vendors, Aircall chose Gladia for its strong performance in transcription accuracy, especially for key strategic languages.
Gladia’s API allowed Aircall to:
✓ Transcribe calls across multiple languages like Spanish, German, and Italian.
✓ Process over 1M transcriptions per week
✓ Deliver transcripts significantly faster than their previous solutions.
How Aircall uses transcription
Aircall integrates Gladia’s transcriptions as a foundational layer for advanced features for their CCaaS platform:
Searchability: Users can search for keywords across calls
AI-generated insights: Summaries, key topics, and sentiment analysis are built on top of the transcripts
Agent coaching: Aircall’s coaching features assess calls for compliance and training, evaluating factors like greetings or responses to objections
CRM Integration: While transcriptions aren’t logged directly into CRMs like HubSpot or Salesforce, summaries and AI insights are pushed via webhooks
Farid explains,
Why Aircall chose Gladia
Aircall’s decision to partner with Gladia was driven by:
Accuracy: High performance across key languages benchmarked on internal datasets composed of phone call audio
Speed: Drastically reduced transcription delays
Developer Experience: A well-designed API that simplified integration
Cost-Effectiveness: A solution that balances performance with the economics of scaling
Results: Faster insights, smoother operations
Since switching to Gladia:
Transcription times have dropped from up to 30 minutes to under 1.5 minutes
Aircall processes around 1M calls weekly, enabling scalable AI features
Improved user satisfaction by delivering faster insights
Farid highlights,
Looking ahead
Aircall is exploring new frontiers with real-time transcription and AI voice agents. While asynchronous transcription currently meets most needs, the team is actively experimenting with new features like real-time assistance during sales calls, where AI can suggest responses based on conversation context.
Farid shares,
Final thoughts
Farid’s advice for companies looking to integrate speech-to-text AI:
About Gladia
Gladia provides a speech-to-text and audio intelligence API for building virtual meeting and note-taking apps, call center platforms, and media products, providing transcription, translation, and insights powered by best-in-class ASR, LLMs, and GenAI models.
After reading this case study, do you think Gladia could be the right fit for your business?
