Transform spontaneous speech into structured intelligence with context-aware AI transcription.
AuraNotes is a cutting-edge AI-native transcription and knowledge synthesis platform designed for the 2026 hybrid workforce. Built on a proprietary fine-tuning of Whisper-v3 Large-v3 and integrated with Claude 3.5/GPT-4o pipelines, AuraNotes moves beyond simple speech-to-text. It features a unique 'Semantic Memory' engine that cross-references new transcripts with historical meeting data to identify recurring themes, conflicting project directives, and long-term action items. Technically, its architecture utilizes edge-computing for initial audio processing to reduce latency, followed by cloud-based diarization for high-fidelity speaker identification in multi-mic environments. Positioned as a direct competitor to Otter.ai and Fireflies, AuraNotes differentiates itself through deep local-first privacy options and an 'Atomic Note' architecture that automatically fragments transcripts into searchable, interconnected insights compatible with Obsidian and Notion. In 2026, it serves as a central hub for corporate intelligence, providing not just summaries, but strategic gap analysis and automated stakeholder briefing reports based on raw verbal inputs.
Uses vector embeddings to link current meeting topics with historical data nodes stored in a private graph database.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Processes initial phoneme recognition on the local device before syncing with cloud-based LLM layers.
Generates distinct summary versions tailored to different audience roles (e.g., Executive, Dev, Marketing).
Detects and translates mid-sentence language switches in real-time across 50+ languages.
Identifies and masks personally identifiable information (SSNs, names, addresses) in the transcript automatically.
Cross-references mentioned deadlines with calendar availability to flag impossible schedules.
A REST API that allows third-party tools to query the semantic meaning of voice data.
Valuable user feedback is often lost in long interview recordings.
Registry Updated:2/7/2026
Maintaining accurate, timestamped records of client conversations for billable hours.
Brief meetings lack documentation of blockers and commitments.