Top 6 agentic features in meeting assistants

Meeting assistants have spent a decade getting better at recording and almost no time getting better at acting. That is finally changing. A new generation of tools is crossing the line from capture to action: they look things up mid-call, intervene when something required is missing, draft the follow-ups, reason across your meeting history, expose meeting context to other AI tools, and synthesize hours of conversation into formats you can actually consume.

How contact center AI improves efficiency: benchmarks and ROI

TL;DR: Manual QA teams review 1–5% of contact center calls; AI-powered platforms can score all of them, but only when the underlying transcript is accurate. WER and DER are the hidden bottlenecks: a wrong name, missed compliance phrase, or misattributed speaker corrupts every downstream system that reads the transcript, from routing and agent assist to post-call summaries and QA scoring. Our Solaria-1 model delivers on average 29% lower WER than alternatives on conversational speech and on average 3x lower DER (diarization error rate), covers 100+ languages including 42 that no other STT API supports, and handles the full audio pipeline (record, transcribe, enrich) in a single API.

AI solutions for call centers without human translators

TL;DR: At an illustrative fully loaded offshore rate of $6–$15/hr, replacing BPO translation at 10,000 hours/month with Gladia's Growth plan brings the estimated cost from $80,000–$150,000 down to approximately $2,000/month, with diarization, translation, NER, and sentiment included at the base rate. Every downstream output is ceiling-bounded by STT accuracy: a single transcription error produces a wrong translation, a wrong CRM entry, and a wrong coaching score. Native code-switching support is the bottleneck most teams discover only in production. Solaria-1 covers 100+ languages, including 42 not available on any other STT API, with mid-conversation code-switching built in from day one.

Call center note-taking tips: how to capture better support conversations

TL;DR: Manual call notes split agent attention and introduce errors that corrupt downstream systems. Structured documentation covering account ID, intent, steps attempted, sentiment, and commitments is the minimum viable baseline. Scaling that standard means replacing manual shorthand with our async API, which returns speaker-labeled, LLM-ready output in a single call, processing approximately one hour of audio per 60 seconds, with no customer audio used for model retraining on Growth and Enterprise plans.

Custom vocabulary vs. custom spelling: which one to choose for better transcripts

Even the most advanced speech-to-text systems make mistakes when they hit brand names, technical acronyms, or non-standard pronunciations. For call centers and customer service platforms, these aren't minor glitches: they break workflows, misrepresent customer needs, and erode trust on both ends of the call.

Build a customer interview library with Gladia, Airtable & Make.com

TL;DR: Most product teams lose qualitative insights to scattered audio and transcripts that misattribute quotes. A reliable interview library needs accurate async diarization, automated routing, and a searchable database. Gladia's Solaria-1 sets the accuracy floor (29% lower WER, 3x lower DER on conversational speech), and Make.com routes its structured JSON into Airtable automatically, turning raw recordings into a searchable, theme-tagged customer content library.

Build an automated sales call analyzer with Gladia and n8n

TL;DR: Off-the-shelf conversation intelligence platforms cost $1,200 to $2,400 per seat per year, while this n8n and Gladia pipeline scales at $0.20 to $0.61 per hour of audio with all features included. The async pipeline handles transcription, speaker diarization, and audio intelligence in a single API call, and the structured JSON output maps directly into HubSpot or Salesforce through n8n nodes. Gladia's Solaria-1 model covers 100+ languages, including 42 that no other API-level competitor supports, protecting CRM data quality for global sales teams.

How to build a no-touch pipeline from sales calls to CRM

TL;DR: Manual CRM entry breaks sales intelligence pipelines because reps skip fields and misremember details, creating corrupted deal data that spreads into forecasts, coaching scores, and follow-up tasks. The bottleneck in fixing this isn't the CRM API or the LLM prompt, it's the transcription layer, since a high word error rate corrupts every entity Claude extracts downstream. This tutorial walks through a production-ready pipeline using Gladia's async STT for transcription, Claude for entity extraction, and n8n for orchestration, with most teams reaching production in under 24 hours. Gladia's Solaria-1 model delivers on average 29% lower WER than alternatives on conversational speech, directly protecting the accuracy of every deal record written to the CRM.