MeetMind is an SDK that lets developers create AI agents that can join live calls and behave like real participants, not just passive note-takers. Unlike tools like Fireflies or Otter that simply transcribe, MeetMind agents understand the context before and during the call, based on structured inputs such as agendas, titles, and documents, so their contributions remain relevant throughout the session.

During a live call, a MeetMind agent can speak, send chat messages when appropriate, adjust its tone and verbosity to avoid over-talking, and wait for its turn before responding. Developers who build on the SDK have full control over this behavior. They can inject context such as documents, prompts, and structured data, define rules for when the agent speaks, how often, and in what tone, connect it to meeting platforms like Google Meet and Zoom, and handle real-time interaction, including audio, chat, and meeting events.

Built on top of this SDK is a real-product use case: an AI-powered Interview Assistant. It pulls candidate data such as the resume, name, and role applied for; understands the job description and interview goals; automatically joins scheduled interview calls; and conducts or assists in interviews intelligently while staying aligned with role requirements and candidate context throughout the session.

INTERVIEW BETTER.REMEMBER EVERYTHING.

Meet AI tools used in meetings today do one of two things: they sit silently recording everything without contributing anything, or they respond to every single message like an overactive bot. Neither is useful. What teams actually need is an AI that knows when to speak, what to say, and when to stay quiet, and nothing on the market does that today.

MeetMind is built to close that gap. Transcription tools like Otter and Fireflies join calls and record everything, but they cannot ask follow-up questions, flag unresolved agenda items, or hold the conversation when the interviewer steps away. They are recorders, not participants.

For recruitment teams, the problem runs deeper: In scale, different interviewers ask different questions, evaluate differently, and fill scorecards hours after the call. The result is inconsistent evaluation and candidate experience that vary wildly depending on who is available.