Speech AI is moving fast, with tons of tools out there claiming super accurate transcription and smart audio features. But if you’re part of a big enterprise—especially in a field full of complex jargon—you know it’s not that simple. Clean, quiet recordings aren’t the norm, and you need more than just words on a page.
In this breakdown, we’re comparing aiOla vs. AssemblyAI with a real-world lens. We’re talking multilingual support, handling noisy conversations with multiple speakers, spotting industry-specific terms, and plugging smoothly into your existing systems. If your team needs speech AI that goes beyond transcription and actually delivers insights you can use, this guide’s for you.
Understanding aiOla vs. AssemblyAI
You may be thinking, “Are we just comparing apples to apples?” However, when looking at aiOla vs. AssemblyAI, you’ll quickly see aiOla is built better for real-world enterprise scenarios. Let’s break down each of these speech AI solutions:
aiOla
aiOla is a speech AI platform built with enterprises in mind. Unlike many speech-to-text providers that focus solely on transcription, aiOla transforms unstructured spoken data into structured, actionable insights. Its unique strength lies in Jargonic, an AI model designed to recognize complex industry jargon right out of the box, without the need for retraining or custom vocabulary input. This is a major differentiator for companies in fields such as healthcare, manufacturing, finance, and logistics, where terminology is dense and specialized.
aiOla supports over 120 languages and dialects, enabling global companies to unify their voice data from diverse regions. Its architecture is optimized for real-world scenarios: noisy workplaces, multi-speaker meetings, and varied acoustic conditions. Beyond transcription, aiOla offers features like:
- Keyword spotting
- Intent recognition
- Real-time alerting
AssemblyAI
AssemblyAI offers a popular developer-centric speech-to-text API, favored for its simplicity and suite of audio intelligence tools, including sentiment analysis, speaker diarization, and content moderation. It targets a broad audience from startups to enterprises but tends to prioritize clean transcription and audio metadata extraction.
AssemblyAI supports automatic language detection across multiple languages but currently offers a more limited language and dialect range compared to aiOla. While AssemblyAI vs. aiOla provides custom vocabulary support for domain-specific terms, this often requires some retraining or fine-tuning to achieve high accuracy.
AssemblyAI is best suited for organizations seeking a powerful, flexible API for transcription and audio analysis but without extensive enterprise-grade jargon handling or workflow automation capabilities.
Comparison of Key Features
When looking at aiOla vs. AssemblyAI, you need to start with their key features. This way, you can determine which one is the better choice for your business. Let’s see how they measure up:
Use Cases: Consumers vs. Enterprises
AssemblyAI performs well in consumer-grade applications where the focus is on clean audio and general speech transcription—think podcasts or user voice notes. aiOla, on the other hand, is purpose-built for enterprise environments. Its Jargonic V2 engine is trained to handle unstructured, jargon-heavy, multilingual speech data in chaotic, real-world settings like warehouses, logistics hubs, and medical facilities—without the need for model retraining.
AMI Meetings: aiOla Jargonic V2 vs AssemblyAI Best
When it comes to automatic meeting insights (AMI), aiOla’s Jargonic V2 stands out. In benchmark tests, it achieved a WER of 15.1% on AMI meetings—outperforming AssemblyAI Best and Whisper V3. This makes it highly effective for enterprises that deal with overlapping dialogue, specialized terminology, and multiple speakers.
English Speech Recognition Across Multiple Datasets
aiOla’s Jargonic V2 consistently outperforms on various English benchmarks, including CommonVoice and AMI. Its focus on keyword spotting and real-world speech gives it a distinct edge. Whether it’s spontaneous dialogue or meetings filled with industry-specific language, Jargonic V2 delivers lower error rates and higher precision.
Word Error Rate (WER) Performance
WER is a fundamental metric for evaluating transcription accuracy. Lower is better—and Jargonic V2 currently leads. It delivers a 5.2% WER across English datasets, beating AssemblyAI Best and even Whisper V3. That’s not just a number—it translates to fewer mistakes, less manual review, and faster time-to-insight for enterprise teams.
CommonVoice V13 (Multilingual Speech)
When tested on CommonVoice V13, aiOla shows robust multilingual capability. It supports 120+ languages and dialects, with especially strong performance in noisy and jargon-rich conditions. AssemblyAI supports fewer languages and often requires custom training for niche dialects or specialized vocabulary.
Earnings Call Dataset
This dataset simulates high-stakes, real-world financial calls that feature dense business language and international accents. aiOla’s Jargonic V2 outperforms AssemblyAI by a wide margin in keyword recall and accuracy. It correctly identifies business-specific terminology across global calls with minimal error—critical for financial compliance and analysis workflows.
Keyword Spotting & Recall
Keyword recall measures how many key terms are correctly transcribed. aiOla’s Jargonic V2 excels here. It delivers higher recall rates on specialized datasets like Earnings-22, which includes global finance terminology, outperforming AssemblyAI in both English and multilingual scenarios:
- German
- French
- Spanish
- Portuguese
No fine-tuning or custom training needed.
Integration
AssemblyAI offers a solid API, but aiOla goes further with seamless integration into enterprise workflows. Its real-time data platform doesn’t just transcribe—it turns spoken data into structured output, complete with alerts, reports, and direct AI model handoffs. This reduces friction and makes adoption straightforward for ops, logistics, and pharma teams.
Number of Supported Languages
aiOla supports over 120 languages and dialects out of the box, which makes it ideal for global teams and decentralized operations. AssemblyAI supports a much smaller list of languages and often lacks regional dialect precision, especially under noisy conditions or when jargon is present.
Efficiency
In enterprise environments, speed and ease of deployment matter. aiOla’s zero-shot learning means there’s no need to retrain for every new domain or vocabulary set—it works immediately. That leads to shorter implementation cycles and more reliable insights across teams. AssemblyAI typically needs custom training for industry-specific use cases.
Accuracy
aiOla’s Jargonic V2 consistently leads in both WER and CER (Character Error Rate), across clean and noisy conditions. Its precision doesn’t fall apart under stress—noisy background, overlapping speakers, or mixed language input. AssemblyAI performs well in clean audio but sees drop-offs in complex scenarios.
Enterprise-Grade Jargon Recognition
aiOla’s zero-shot keyword spotting makes it a unique leader in jargon-heavy fields like pharma, logistics, and manufacturing. It can recognize terms like “cold chain logistics” or “biopharma compliance” without needing additional training. AssemblyAI typically requires curated training datasets to recognize this kind of terminology with comparable accuracy.
Noisy, Multi-Speaker Environments
Real-world conditions aren’t quiet like a controlled studio—and aiOla is built for that. Whether it’s forklift beeps, multiple accents, echoey conference rooms, or field service radios, Jargonic V2 thrives where others fail. AssemblyAI works well with single-speaker, clean input but struggles with noise-heavy environments and overlapping conversations.
Data Privacy & Compliance
Both platforms are privacy-conscious, but aiOla adds a layer of protection with built-in Named Entity Recognition (NER). This ensures sensitive data like patient names or account numbers are properly redacted or flagged—automatically. It’s ideal for teams dealing with compliance-heavy sectors like healthcare and finance.
So, What’s the Verdict on aiOla vs. AssemblyAI?
When it comes to speech AI, aiOla vs. AssemblyAI take very different approaches. AssemblyAI performs well in clean, general-use transcription, but aiOla is built for enterprise-grade challenges—handling jargon, multilingual input, noisy environments, and unstructured speech with zero-shot learning and no retraining needed.
Jargonic V2, aiOla’s proprietary engine, delivers industry-best accuracy, especially in keyword spotting and real-time structured data output. For organizations that need more than transcription—like actionable insights and workflow automation—aiOla stands out. If your operations rely on complex, high-volume voice data, aiOla is purpose-built to deliver precision, scale, and speed where other platforms fall short.
Ready to see it in action? Book a demo and discover how aiOla can turn your spoken data into enterprise intelligence.
