API Comparison Table

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MiFID II and FCA call recording: compliance for voice transcription in finance

TL;DR: Financial firms operating under MiFID II and FCA jurisdictions must maintain searchable, high-accuracy records of all client-related voice communications, including remote and mobile calls under FCA Market Watch 66. Engineering and product teams building for these obligations typically implement dedicated cloud infrastructure with defined data residency, controls that prevent customer audio from being used to retrain models on Growth and Enterprise plans, and transcription accurate enough that the resulting records hold up under regulatory review. Transcription and speaker attribution errors are not product quality issues in this context. They are audit trail failures, and regulators treat them as such.

Speech-To-Text

Why speech-to-text accuracy matters upstream of your LLM

TL;DR: Downstream LLM performance is ceiling-bounded by upstream transcription accuracy. A transcript with a meaningful error rate doesn't produce proportionally degraded summaries or CRM entries. It produces outputs where hallucinated names, inverted logic, and misattributed speaker turns compound silently into every downstream system that reads them. Prompt engineering cannot recover information the STT layer never captured. Gravite cut call quality review time by 93%, from 15 minutes to 1 minute per call, once transcript accuracy was high enough to trust the output without manual verification.

Speech-To-Text

Telephony-audio robustness: why 8kHz narrowband calls break generic STT

TL;DR: Telephony audio is constrained to 8kHz narrowband frequencies, stripping away the high-frequency spectral energy that generic 16kHz STT models require. Standard upsampling cannot recover phonemes that were never captured at the source, leading to transcription errors that compound silently into downstream NLU failures. Solaria-3 ranks #1 on Switchboard, the most challenging conversational telephony dataset, ahead of AssemblyAI, ElevenLabs, Deepgram, Mistral, and Speechmatics, ensuring your downstream LLM pipelines receive clean, structured data from real-world noisy call audio.

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: