Cogram

Cogram integrates with virtual meeting platforms (Zoom, Teams, Google Meet) via native plugins or API webhooks to capture audio streams in real-time, applies automatic speech recognition (ASR) with speaker identification to distinguish between participants, and produces timestamped transcripts with speaker labels. The system likely uses cloud-based ASR engines (Google Cloud Speech-to-Text, Azure Speech Services, or proprietary models) with post-processing to handle meeting-specific vocabulary and context.

Cogram applies natural language processing (NLP) and named entity recognition (NER) to identify action items, decisions, and commitments from the meeting transcript. The system uses pattern matching and semantic understanding to extract task descriptions, infer responsible parties from context (e.g., 'John will handle the API integration'), detect deadlines mentioned in conversation, and structure these into actionable items. This likely involves fine-tuned language models trained on meeting corpora to recognize action item linguistic patterns ('we need to', 'I'll take that', 'by next Friday').

Cogram generates abstractive summaries of meeting transcripts using sequence-to-sequence language models (likely transformer-based, similar to BART or T5 architecture) that condense the full transcript into concise overviews. The system supports multiple summary formats: executive summaries (key decisions and outcomes), detailed summaries (discussion flow with context), and topic-based summaries (organized by agenda item). Customization options allow users to specify summary length, focus areas, and tone, with the model adapting output accordingly.

Cogram implements native integrations with major meeting platforms (Zoom, Microsoft Teams, Google Meet) through OAuth-based authentication and platform-specific APIs. The system uses webhooks or real-time event streams to detect meeting starts, automatically joins meetings (as a bot participant or via API), captures audio/video streams, and handles cleanup after meeting ends. Integration architecture likely uses adapter pattern to abstract platform-specific API differences, allowing unified handling of Zoom's Recording API, Teams' Call Records API, and Google Meet's recording capabilities.

Cogram exports meeting artifacts (transcripts, summaries, action items) to external systems via REST APIs or native integrations with popular productivity tools (Slack, Jira, Asana, Notion, Microsoft Teams). The export pipeline transforms Cogram's internal data structures into platform-specific formats (Slack messages, Jira tickets, Asana tasks) and handles authentication with target systems. This enables action items to automatically create tasks in project management tools, summaries to post to team channels, and transcripts to be stored in knowledge bases.

Cogram indexes meeting transcripts, summaries, and action items in a searchable database using full-text search and semantic embedding techniques. Users can search across all historical meetings using keyword queries ('budget discussion', 'Q4 planning') or semantic queries ('what was decided about pricing?'). The system likely uses vector embeddings (from models like Sentence-BERT or OpenAI embeddings) to enable semantic similarity matching, allowing users to find conceptually related meetings even with different terminology. Search results include meeting date, participants, relevant transcript excerpts, and associated action items.

Cogram analyzes meeting transcripts to generate analytics about participant engagement, speaking time distribution, and contribution patterns. The system uses speaker diarization data to calculate metrics like total speaking time per participant, number of contributions, average contribution length, and sentiment of contributions. Advanced analytics may include topic expertise inference (identifying who speaks most about specific topics), decision influence analysis (whose suggestions were adopted), and engagement trends over time. This data is presented via dashboards or exported as reports.

Cogram implements compliance features for regulated industries including automatic data retention policies, encryption at rest and in transit, audit logging of who accessed meeting data, and GDPR/CCPA-compliant data deletion workflows. The system supports configurable retention periods (e.g., delete meetings after 90 days), role-based access control to restrict who can view specific meetings, and compliance reporting for audits. Meeting data is encrypted using industry-standard algorithms (AES-256), with encryption keys managed via key management services (AWS KMS, Azure Key Vault).