Krisp

Intercepts audio streams at the application or driver level during active communication sessions and applies real-time noise suppression to remove background noise, echo, and cross-talk before audio reaches the listener. Processing occurs locally on the client device to minimize latency, with claims of sub-500ms processing overhead. The system operates transparently across any communication application (Zoom, Teams, Google Meet, etc.) without requiring application-specific plugins.

Converts spoken audio to text in real-time during active meetings, displaying captions as participants speak. The system captures audio from the communication application, processes it through a speech-to-text model (model type and training data unknown), and streams transcripts to the user interface with claimed support for multiple languages. Transcripts are stored in Krisp's cloud system for post-meeting access and integration with downstream tools via webhooks or API.

Exposes voice translation as an API endpoint in the Krisp Voice AI SDK, allowing developers to programmatically translate audio from one language to another in voice applications and AI agents. The API accepts audio input in the source language and returns audio output in the target language. Supported language pairs, translation quality, and latency are not disclosed. Likely used for enabling multilingual voice agents or real-time translation in voice applications.

Exposes noise cancellation as an API endpoint in the Krisp Voice AI SDK, allowing developers to programmatically remove background noise from audio streams in voice applications and AI agents. The API accepts noisy audio input and returns cleaned audio with noise suppressed. The noise cancellation algorithm, supported noise types, and effectiveness metrics are not disclosed. Likely used for improving speech recognition accuracy or voice quality in voice applications.

Provides real-time AI assistance to call center agents during active customer calls, offering suggestions, guidance, or information to improve call quality and customer satisfaction. The system analyzes the call in real-time, detects customer intent or issues, and provides contextual suggestions to the agent via a sidebar or dashboard. The AI model, suggestion generation approach, and integration with call center systems (Genesys, Avaya, etc.) are not disclosed. Pricing and feature details are completely unknown.

Analyzes call center recordings to extract insights on call quality, compliance, and agent performance. The system processes recorded calls (audio and transcripts) to generate call scores, detect compliance violations, identify training opportunities, and track agent performance metrics. The analytics model, scoring methodology, and compliance rule definitions are not disclosed. Pricing and feature details are completely unknown.

Enhances the conversational flow of AI voice agents by improving turn-taking behavior (detecting when the user has finished speaking and the agent should respond). The system analyzes audio and speech patterns to determine optimal response timing, reducing awkward silences or interruptions. The algorithm and accuracy metrics are not disclosed. Likely used to improve the naturalness of voice agent interactions.

Processes the complete meeting transcript and audio after the meeting concludes, generating a natural language summary of key discussion points and extracting a structured list of action items with implied owners or deadlines. The summarization model type, training approach, and context window size are not disclosed. Summaries are generated server-side and stored in Krisp's cloud system, with export to integrations (Slack, HubSpot, Pipedrive, Zapier) via webhook API.

Applies real-time audio transformation to modify the speaker's accent or the listener's perception of accents during active calls. The technical approach (voice synthesis, audio filtering, or speech-to-speech conversion) is not disclosed. Speaker-side conversion modifies the speaker's own voice before transmission; listener-side conversion modifies received audio for the listener. Processing occurs locally on the client device to minimize latency. Core plan limited to 1 hour/day; higher tiers offer unlimited usage.

Converts spoken audio from one language to another language in real-time during active calls, with audio output in the target language. The system captures audio, processes it through a speech-to-speech translation model (model type, language pairs, and latency unknown), and outputs translated audio to the listener. Processing approach (cascade of ASR + MT + TTS vs. direct speech-to-speech) is not disclosed. Likely a Pro/Business plan feature based on pricing structure.

Records the complete meeting session including audio and video streams from the communication application, storing the recording in Krisp's cloud system for post-meeting access. Recording is initiated automatically when the user joins a meeting and continues until the meeting ends. Recordings are stored with metadata (date, duration, participants) and are accessible via the Krisp dashboard. Export format and storage duration limits are unknown.

Provides an interactive chat interface where users can ask questions about a completed meeting, with responses generated from the meeting transcript and recording. The system uses the stored transcript and audio as context for a conversational AI model (model type and training approach unknown) to answer user queries about discussion points, decisions, action items, or specific statements. Chat is available post-meeting in the Krisp dashboard.

Exposes meeting data (transcripts, summaries, action items, recordings) via webhook events that are sent to external systems in real-time or on-demand. Developers can configure webhook endpoints to receive JSON payloads containing meeting metadata, transcript segments, summaries, and action items. Integration is available with Zapier, HubSpot, Slack, Pipedrive, and Affinity, with custom webhooks for arbitrary HTTP endpoints. Webhook payload schema and retry logic are not documented.

Isolates the primary speaker's voice from background noise and other speakers in audio streams, enabling AI voice agents to receive cleaner input for speech recognition and processing. This is a server-side or client-side audio processing capability exposed via the Krisp Voice AI SDK for developers building voice applications. The isolation algorithm and accuracy metrics are not disclosed. Likely used to improve speech recognition accuracy for AI agents in noisy environments.

Exposes accent conversion as an API endpoint in the Krisp Voice AI SDK, allowing developers to programmatically convert accents in audio streams for voice applications and AI agents. The API accepts audio input and returns accent-converted audio in the target accent. Supported accent pairs, conversion quality, and latency are not disclosed. Likely used for improving voice agent naturalness or accessibility in voice applications.