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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.

Speech-To-Text

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.

Speech-To-Text

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.

Ebook: Ultimate guide to using LLMs with speech recognition

Published on Jan 7, 2025

Large Language Models (LLMs) have enabled businesses to build advanced AI-driven features, but navigating the many available models and optimization techniques isn't always easy.

If you’re looking to combine speech recognition (STT) and LLMs for cutting-edge voice apps, look no further! Our ultimate guide is finally here, and it’s filled with valuable strategies and hands-on insights from our work with hundreds of audio-first companies and extensive interviews with experts in AI note-taking, sales enablement and customer support.

What you'll learn: