Private On-Device Transcription for Qualitative Research

Evidano is an AI-powered qualitative data analysis platform that ingests private on-device transcripts and produces thematic and visual insights without sending data to third parties. Epilude announced Model 4 on July 14, 2026, a local cleanup model that runs on Apple Silicon, installs as a 1.5 GB package, and reduced critical meaning errors by 56% (from 9 to 4) on Epilude’s internal 90-scenario benchmark.

• Model 4 runs offline on Apple Silicon Macs, preserving audio and transcript privacy while performing local cleanup.
• Model 4 reduced critical meaning errors by 56% (from 9 to 4) on Epilude’s internal 90-scenario benchmark, improving trust in automated transcripts.
• Full cleanup with Model 4 requires Apple Silicon and 16 GB of RAM, otherwise expect basic formatting and offline transcription only.
• Evidano ingests cleaned, on-device transcripts and performs thematic, cross-segment, and visual analyses without sending data to third-party models.

Epilude announced Model 4 on July 14, 2026.

Model 4 is a new on-device cleanup model that runs locally in Epilude’s Local Mode and aims to make dictation transcripts cleaner without leaving a Mac. Read the original post at Epilude.

• On-device cleanup model (Model 4) built on the Qwen family, same 1.5 GB footprint and speed.
• Critical meaning errors down 56% (Model 3: 9 → Model 4: 4) on Epilude’s internal 90-scenario benchmark.
• Full cleanup requires Apple Silicon Macs with 16 GB RAM; otherwise transcription plus basic formatting runs offline.

Model 4 is a cleanup model that takes speech-to-text output and sequences it into sendable writing, fixing punctuation, removing filler, resolving false starts, and preserving speaker tone or mixed-language phrases.

Epilude’s evaluation focuses on zero-tolerance critical errors (meaning changes, instruction leaks); Model 4 passes nine more benchmark scenarios than Model 3 and reduces critical errors from 9 to 4.

• Deterministic decoding: outputs are stable across runs, which matters for reproducible coding.
• Privacy-first: everything runs locally on Apple Silicon, useful when consent or regulation prohibits cloud audio processing.
• Known limits: long, rambling inputs still trigger most residual failures; cloud models still outperform on some long-input metrics.

Follow these steps to turn private, on-device transcripts into research outputs.

• 1) Export cleaned transcripts from Local Mode (Model 4) as UTF-8 text or time-stamped JSON.
• 2) Upload files to Evidano via secure, encrypted import and set a custom dictionary for names and terms (Evidano).
• 3) Run automatic cleanup and PII redaction if required, then import or build a codebook.
• 4) Use AI-assisted coding to apply codes at scale and review edge cases flagged by the platform.
• 5) Generate thematic summaries, cross-segment frequency tables, and co-occurrence visuals.
• 6) Export quotes and visuals into a stakeholder slide deck or decision memo.

Model 4 improves many failure cases but still struggles with long, rambling passages, so plan a QA pass for long monologues or narrative interviews.

For highly sensitive clinical or legal work, retain human review and explicit consent; Evidano supports PII redaction and access controls to help with this.

• Model 4 requires Apple Silicon and 16 GB RAM for full cleanup; for smaller machines expect basic formatting only.
• Epilude’s benchmark is internal and not directly comparable to external metrics, so validate on a held-out sample from your corpus before full automation.

Epilude’s Model 4 demonstrates that high-quality cleanup and strict privacy are not mutually exclusive.

For qualitative teams, that means transcripts you can trust and analyze without moving audio off-device; to turn local transcripts into reproducible themes, segment comparisons, and stakeholder-ready visuals, Try Evidano for free.