Free
$0Free plan available.
Aimbly is a solution designed for busy professionals to quickly and accurately generate insights from data and create meeting summaries without compromising security and privacy. It automates data analysis, meeting transcripts, summaries, and action items, providing a comprehensive overview while ensuring data protection. Aimbly aims to transform meetings from chaotic to productive, offering features powered by GPT-4.
Use Aimbly to automatically transcribe meetings, generate summaries and action items, and analyze data. Simply integrate Aimbly, and it will handle the rest, providing insights and summaries at a glance.
No. While the transcription is only visible to you, you have the option to share the email with all participants in the meeting you are transcribing.
Aimbly is committed to protecting your information security; only you can access your data unless you choose to share it.
Aimbly does not record or store audio. Transcripts are generated in real-time and saved as text, accessible only by you.
Yes, we can assist you with that. Please contact us at hello@aimbly.co.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Aimbly.
Compare Gemini 1.0 Pro Deprecated and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 2.0 Flash Lite and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 1.0 Pro Deprecated and Gemini 1.5 Flash Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus general-purpose AI workloads.
Compare Gemini 1.0 Pro Deprecated and Gemini 1.5 Flash Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus general-purpose AI workloads.
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