Fathom is an AI meeting assistant that helps users capture, summarize, search, and act on meetings with less manual work. The platform creates accurate transcripts, instant summaries, action items, and follow-up notes so users can focus on live conversations instead of taking notes. Fathom supports both traditional meeting capture and bot-free capture through its desktop app. Teams can use Fathom as a shared source of truth across customer calls, internal meetings, strategy sessions, and project conversations. Ask Fathom lets users search across meetings and ask questions about conversations, decisions, commitments, risks, and next steps. The platform also supports topic monitoring so important moments and signals are easier to find. Fathom syncs meeting notes, insights, and action items into tools such as Slack, Salesforce, HubSpot, Notion, Asana, Gmail, Zoom, Google Meet, Microsoft Teams, ChatGPT, Claude, Zapier, and API or MCP workflows. It supports security and compliance needs with SOC 2 Type II, GDPR, HIPAA compliance, SSO, and SCIM. By combining AI notetaking, bot-free capture, transcripts, summaries, integrations, search, and workflow automation, Fathom helps teams move from meetings to execution faster.
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An API powered by Google's AI technology allows you to accurately convert speech into text. You can accurately caption your content, provide a better user experience with products using voice commands, and gain insight from customer interactions to improve your service. Google's deep learning neural network algorithms are the most advanced in automatic speech recognition (ASR). Speech-to-Text allows for experimentation, creation, management, and customization of custom resources. You can deploy speech recognition wherever you need it, whether it's in the cloud using the API or on-premises using Speech-to-Text O-Prem. You can customize speech recognition to translate domain-specific terms or rare words. Automated conversion of spoken numbers into addresses, years and currencies. Our user interface makes it easy to experiment with your speech audio.
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Gemini 3.1 Flash-Lite
Gemini 3.1 Flash-Lite represents Google’s newest addition to the Gemini 3 family, built specifically for speed and affordability at scale. Engineered for developers managing high-frequency workloads, the model balances performance and cost efficiency without sacrificing quality. It is competitively priced at $0.25 per million input tokens and $1.50 per million output tokens, making it accessible for large production deployments. Compared to Gemini 2.5 Flash, it delivers substantially faster responses, including a 2.5x improvement in time to first token and a 45% boost in output speed. Benchmark evaluations show strong results, with an Elo score of 1432 and leading scores in reasoning and multimodal understanding tests. The model rivals or surpasses similarly tiered competitors while even outperforming some previous-generation Gemini models. A key feature is its adjustable reasoning control, enabling developers to fine-tune how much computational “thinking” is applied to each request. This flexibility makes it ideal for both lightweight tasks like translation and more complex use cases such as dashboard generation or simulation design. Early enterprise adopters have praised its ability to follow instructions accurately while handling complex inputs efficiently. Gemini 3.1 Flash-Lite is currently rolling out in preview within Google AI Studio and Vertex AI for enterprise customers.
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Amazon Transcribe
Amazon Transcribe simplifies the integration of speech-to-text features for developers looking to enhance their applications. Analyzing and searching audio data presents significant challenges for computers, making it essential to convert spoken words into written format for effective usage in various applications. Traditionally, businesses had to collaborate with transcription services that imposed costly contracts and were complicated to integrate with existing technology, making the transcription process cumbersome. Moreover, many of these services relied on outdated technologies that struggled to handle specific situations, such as the low-quality audio typical in contact center environments, leading to decreased accuracy. In contrast, Amazon Transcribe utilizes an advanced deep learning technique known as automatic speech recognition (ASR) to convert speech into text efficiently and with high precision. This service is versatile, allowing for the transcription of customer service interactions, the automation of subtitling, and the creation of metadata for media files, ultimately resulting in a comprehensive and searchable archive of content. With its user-friendly design and robust capabilities, Amazon Transcribe stands out as an essential tool for developers aiming to enhance the functionality of their applications.
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