AssemblyAI

What do you like best about the product?

What I like most about AssemblyAI is how easy and dependable it is to use. The transcription quality is accurate, the API is straightforward to work with, and the features are genuinely useful. It also saves me time and makes it simpler to build applications that handle audio and speech.

What do you dislike about the product?

One thing I don’t like about AssemblyAI is that some of the more advanced features can take a bit of time to fully understand and get set up properly. Pricing can also become a concern as usage grows, since costs may add up when I start using it more heavily.

What problems is the product solving and how is that benefiting you?

It solves the problem of turning audio and speech into accurate, useful text without me having to build the entire speech-to-text system myself. It saves me a lot of development time and makes it easier to work with recordings, transcripts, and voice data.

What do you like best about the product?

What I like best about AssemblyAI is its highly accurate speech-to-text capabilities combined with a comprehensive set of AI speech understanding features. Beyond transcription, it offers speaker diarization, summarization, sentiment analysis, topic detection, entity extraction, content moderation, and real-time streaming through a simple developer-friendly API. I also appreciate its excellent documentation, fast processing speeds, multilingual support, and seamless integration into applications. Overall, AssemblyAI significantly simplifies building voice-enabled products, reduces development time, and delivers reliable speech intelligence for production applications.

What do you dislike about the product?

One area where AssemblyAI could improve is providing more advanced built-in workflow orchestration, observability, and cost management tools for large-scale production deployments. While the transcription accuracy and speech intelligence capabilities are excellent, debugging complex pipelines and optimizing API usage across high-volume applications can require additional external tooling. I'd also like to see more customizable post-processing pipelines, richer analytics dashboards, and expanded integrations with enterprise platforms. Overall, the experience has been very positive, but enhanced observability, stronger cost visibility, and more advanced workflow management would make AssemblyAI even more valuable for production AI applications.

What problems is the product solving and how is that benefiting you?

AssemblyAI solves the challenge of converting unstructured audio into accurate, actionable data through AI-powered speech recognition and speech understanding. Instead of manually transcribing recordings or building complex speech processing pipelines, developers can use a single API to generate high-quality transcripts, speaker labels, summaries, sentiment analysis, topic detection, entity extraction, and other insights from audio and video content. This reduces development time, improves accuracy, accelerates product delivery, and enables teams to build voice-enabled applications more efficiently. As a result, it has streamlined audio processing workflows, increased developer productivity, and made it much easier to extract meaningful insights from spoken conversations at scale.

What do you like best about the product?

Our company deals in more than 35+ countries and multiple languages, so it becomes very important for us for speech to text in language converted values and that helps our customer support to perform well. It also helps us to put speech into well structured data set which helps us in doing various analysis and improve our offerings.

What do you dislike about the product?

Its a good product but it comes with some drawbacks like vendor dependent setup, if any changes happens at their level, we also need to improve our setup and that creates a huge problem if it is not managed well. Cost also in terms of hours + LLM processing is huge, and with huge operations it is super expensive tool

What problems is the product solving and how is that benefiting you?

We are using this at almost all voice related support on all our sales channels and that is eliminating a lot of basic queries to be responded and with that 40% of responses are already automated and good customer feedback on same. We are able to also record the senitments of customer and their feedback and that helps us to forecast our demands as well.

What do you like best about the product?

I like that AssemblyAI is easy to use and works well for speech and audio. It saves time and makes things easier to build and manage.

What do you dislike about the product?

Sometimes the setup can be a little difficult, especially in the beginning. Also, some features could be more simple and easier to understand.

What problems is the product solving and how is that benefiting you?

AssemblyAI helps us handle speech and audio data without doing everything manually. It saves time, makes the work faster, and helps us build things more easily.

What do you like best about the product?

Developer friendly platform,focus on real world application

What do you dislike about the product?

I dislike about there complex onboarding

What problems is the product solving and how is that benefiting you?

It solve my speech automation problem and it helpful for me

What do you like best about the product?

I really like the robust insights and call analytics that this tool provides which leads to greater understanding of the customer needs and helps improve the behavior of employees when responding to customers.

What do you dislike about the product?

No major problems encountered in the use of this tool and it does not include any complicated functions or tools.

What problems is the product solving and how is that benefiting you?

The accurate call summaries have helped us easily understand customer needs and trends and the text generated from calls helps write emails when answering customer inquiries.

What do you like best about the product?

AssemblyAI's precise word level timestamping and speaker diarization make generating multi-language captions and searchable transcripts for our OTT video catalog effortless. Its high-accuracy models handle noisy background audio in stream tracks seamlessly, drastically reducing manual subtitle post-editing time.

What do you dislike about the product?

Real-time streaming transcription currently supports fewer concurrent languages compared to async processing, which can limit live multi-region OTT broadcasts. Additionally, stacking multiple audio intelligence add-ons like entity detection and topic modeling can cause costs to scale quickly on high-volume catalog runs.

What problems is the product solving and how is that benefiting you?

Assembly AI solves the massive friction and cost of manually captioning and indexing large video catalogs. It benefits our OTT platform by automatically generating frame-accurate captions, enabling full-text searchability aross stream archives, and speeding up global content distribution.

What do you like best about the product?

What I like most about AssemblyAI is its accurate speech-to-text API and the developer-friendly approach to adding voice intelligence to applications. Accurate transcription with support for different accents and speaking styles. Useful features like speaker identification, summarization, and content moderation. Simple APIs that make it easy to integrate speech capabilities into applications. Good handling of real-time and pre-recorded audio. Helpful developer documentation and SDK support. The biggest advantage is how quickly I can turn audio into structured, usable data without building a speech recognition system from scratch. Overall, AssemblyAI saves development time and makes it much easier to add transcription and AI-powered audio analysis to applications.

What do you dislike about the product?

The biggest drawback is occasional transcription errors in challenging audio conditions. The API is easy to integrate, but critical transcripts still need validation when accuracy is important. Speaker diarization isn't always perfect when people talk over each other. Advanced audio intelligence features can increase overall usage costs. More fine-grained customization of transcription behavior would be useful for specialized domains.

What problems is the product solving and how is that benefiting you?

AssemblyAI solves the challenge of processing and understanding large amounts of audio and speech data without having to build and maintain a speech AI system from scratch. Converts speech to text quickly and accurately. Identifies different speakers through diarization.
Extracts useful insights such as summaries and key topics from conversations. Supports real-time transcription for voice-based applications. Provides APIs that make integrating speech intelligence into applications straightforward. In my workflow, AssemblyAI helps turn raw audio into structured, searchable information that I can use for testing, analysis, documentation, or AI-powered features. It saves significant development effort compared with building speech recognition and audio-processing infrastructure internally. The biggest benefit is faster development of voice-enabled applications. AssemblyAI handles the complex speech-processing layer so I can focus on building the actual product experience and business logic.

What do you like best about the product?

The ability to run LLM prompts directly over clean, timestamped audio data without having to build a custom RAG pipeline is a massive timesaver.

What do you dislike about the product?

High-volume usage can get expensive quickly when layering on intelligence features, and real-time streaming supports fewer languages than their batch API.

What problems is the product solving and how is that benefiting you?

It replaces the mess of building custom speech pipelines by turning raw audio into structured data like speaker labels, summaries, and searchable text out of a single API.

What do you like best about the product?

Its specialized models excel at handling challenging audio conditions, including heavy background noise, multiple people talking over each other, and diverse accents.

What do you dislike about the product?

While its asynchronous, pre-recorded transcription supports over 99 languages, its real-time live streaming model is currently limited to a handful of core languages (like English, Spanish, French, and German).

What problems is the product solving and how is that benefiting you?

Beyond just text transcription, it offers built-in tools for Speaker Diarization (identifying who said what), sentiment analysis, automatic topic detection, and summarizing long recordings.