Airgram
Turn meetings into actionable insights with AI-powered transcription and automated summaries.
Turn every conversation into a searchable, queryable vector database for your organization.
MeetVector is an advanced meeting intelligence platform that utilizes high-dimensional vector embeddings to transform raw audio and video data into a structured organizational knowledge graph. Unlike traditional transcription services that offer linear text logs, MeetVector treats every spoken sentence as a data point in a semantic space, allowing for hyper-accurate retrieval-augmented generation (RAG) and cross-meeting pattern analysis. By 2026, MeetVector has positioned itself as the 'Organizational Brain,' enabling teams to query their entire meeting history with natural language prompts (e.g., 'What were the recurring objections in the Q3 sales cycle?') with sub-second latency. The technical architecture supports multi-modal ingestion, processing diarized speech through transformer-based models to capture sentiment, intent, and actionable commitments. Its 2026 roadmap focuses on autonomous agent integration, where MeetVector not only records but also executes workflows in connected CRMs and project management tools based on verbal triggers, effectively bridging the gap between verbal communication and digital execution.
Uses OpenAI/Cohere embeddings to index meeting fragments into a vector database (Pinecone/Weaviate backend), allowing queries based on meaning rather than keywords.
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.
Analyzes tone, pitch, and transcript context to score participant engagement and sentiment trends over time.
LLM-driven extraction of meeting data into structured JSON that maps directly to Salesforce Opportunity or Account fields.
Identifies entities (people, products, competitors) across different meetings to visualize relationships and recurring topics.
Biometric-lite speaker identification that ensures 99% accuracy in diarization even in noisy environments.
Low-latency processing that identifies commitments and assigns them to Jira/Asana tasks before the meeting ends.
Isolation of organizational embeddings in a dedicated cloud instance to ensure zero data leakage between customers.
Information loss between Account Executives and Customer Success Managers during account transitions.
Registry Updated:2/7/2026
Product Managers struggling to sort through hundreds of hours of user interviews.
Verifying that financial advisors or medical staff followed regulatory scripts during calls.