Technology

Audio Capture

Microphone input is captured via Audio Queue Services (AudioToolbox). System audio for meeting recording uses Core Audio process taps (CATapDescription + AudioHardwareCreateProcessTap), which capture at the OS level without injecting into individual applications. Both paths produce 16 kHz mono Float32 PCM.

Echo Cancellation

During meeting recording, the microphone picks up both the local speaker and remote participants' audio from the speakers. A DTLN (Dual-signal Transformation LSTM Network) model runs on CoreML to remove the system audio loopback from the microphone signal before it reaches the ASR model, using the system audio stream as a reference. No AGC or noise suppression is applied — these degrade ASR accuracy.

Speech Recognition

Transcription uses NVIDIA's Parakeet TDT 0.6B model, converted to CoreML format with a Token-and-Duration Transducer (TDT) architecture and FastConformer encoder. The model supports 25+ languages and all inference runs locally on Apple Silicon. During meeting recording, dual parallel ASR workers process the microphone and system audio streams independently.

For streaming transcription, a custom harness processes audio in overlapping windows and merges results across window boundaries to maintain accuracy at chunk edges. The decoder, joint network, and preprocessor run through an accelerated BNNS backend, bypassing CoreML's per-inference overhead for lower latency.

Speaker Diarization

After a meeting ends, an offline diarization pipeline runs in the background while the live transcript is already displayed. A pyannote-based segmentation model identifies speaker turn boundaries, then a WeSpeaker v2 embedding model extracts 256-dimensional voice embeddings for each segment. Clustering uses agglomerative hierarchical clustering (AHC) with centroid linkage, refined by a Variational Bayes HMM (VBx) pass over PLDA-whitened features.

Clustered embeddings are matched against stored speaker profiles using cosine similarity. When a confident match is found, the speaker is identified automatically. Ambiguous cases are resolved by an LLM disambiguation step using conversation context. Profiles accumulate samples across meetings, improving recognition over time.

LLM Post-Processing

After transcription, text is sent to Google's Gemini 3 Flash for refinement: grammar correction, filler word removal, and tone adjustment. Meeting transcripts are additionally processed for speaker attribution, summarization, and action item extraction. Static prompt content is passed as system instructions to take advantage of Gemini's implicit caching, reducing latency on repeated calls.

Only text is sent to the API — audio remains on-device. This step requires an internet connection; raw transcription works fully offline.

Speaker attribution accuracy across 52 audio files from 4 public corpora — earnings calls, panel discussions, and oral arguments. Compares on-device diarization alone vs. the full pipeline with LLM speaker attribution.

Real-time briefing quality across 96 scenarios — sales discovery, customer success, standups, interviews, and long meetings. Evaluates action items, hallucination resistance, question detection, and interpersonal awareness using dual LLM judges.

Reference document RAG pipeline across 15 document types — CRM records, scraped webpages, SEC filings, PDFs, tracked-changes contracts, strategic plans, competitive intel, and messy meeting notes. Tests whether relevant chunks are retrieved accurately during live meetings, even from noisy real-world sources.

Hybrid semantic and full-text search across 26 meetings with 138 queries. Tests recall and ranking accuracy for finding past meetings by topic, action item, or conversational phrase.

Side-by-side pricing against pasting raw transcripts into Claude, GPT-5, Gemini, and DeepSeek — single-meeting summaries, multi-step Q&A, and cross-meeting search. Quality judged on the same rubric.