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Turn meetings into actionable insights with AI-powered transcription and automated summaries.
Turn meetings into searchable intelligence with contextual RAG-powered memory.
MeetTrace is a high-performance AI meeting intelligence platform designed for the 2026 enterprise landscape, where meeting data serves as the foundational corpus for organizational knowledge. The platform utilizes a multi-LLM architecture, integrating models like GPT-4o and Claude 3.5 Sonnet to provide hyper-accurate transcriptions and sentiment-aware summaries. Unlike first-generation transcription tools, MeetTrace employs Retrieval-Augmented Generation (RAG) to allow users to query their entire meeting history as a unified knowledge base. It handles complex speaker diarization and noise cancellation at the edge, ensuring clear data capture even in low-bandwidth environments. Its 2026 market position is defined by its 'Contextual Sync' capability, which doesn't just summarize meetings but automatically maps specific insights into CRM fields and project management boards without manual intervention. The technical infrastructure is built on SOC2 Type II compliant pipelines, offering end-to-end encryption for sensitive corporate communications. By automating the transition from synchronous verbal discussion to structured asynchronous documentation, MeetTrace effectively eliminates 'meeting debt' for distributed teams.
Uses vector embeddings to allow users to ask questions like 'What did the client say about budget in the last three calls?' across all stored transcripts.
Turn meetings into actionable insights with AI-powered transcription and automated summaries.
Transform long-form video content into actionable technical abstracts and structured knowledge bases.
The world's highest accuracy AI assistant for instantly summarizing videos, lectures, and documents.
Transform meeting noise into actionable intelligence with AI-driven knowledge synthesis.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Utilizes voiceprint analysis to identify speakers with 99.2% accuracy, even in rooms with multiple participants on one microphone.
Heuristically identifies sales data (budget, timeline, authority) and pushes it via API to CRM platforms.
Analyzes tone and linguistic markers to plot sentiment shifts throughout a meeting.
Reduces latency by processing audio streams at the nearest edge node before sending text to the LLM.
Uses NER (Named Entity Recognition) to identify tasks and assign them to specific users in Jira or Linear.
Automatically identifies and masks Personally Identifiable Information in transcripts for compliance.
Sales reps forget crucial technical requirements mentioned during calls.
Registry Updated:2/7/2026
Valuable product insights are lost across dozens of user interviews.
Long-form board meetings need concise, legal-grade minutes.