The Two Ways to Run Transcription 

Every transcription app chooses between two fundamentally different technical approaches, and the choice affects cost, complexity, and user experience far more than which provider gets picked. 

Batch transcription sends a finished audio file to the provider and gets text back afterward. Lexi and Note Taker both work this way, which keeps the recording screen simple and lets the transcription run in the background while a user moves on to something else. Real time streaming transcription shows text as people speak, which is what Polly does, tagging each speaker as the conversation happens. On device transcription through Apple's Speech framework or a similar system API exists and avoids sending audio anywhere, but Neon Apps has not shipped this approach. Published guidance from Apple and Google suggests it becomes the right call mainly when offline use or strict data residency is a hard requirement, not a default choice. 

The right approach depends on what the user is actually doing while the app runs: 

• Choose batch transcription for lectures, calls, and any recording a user reviews after the fact, since the simpler pipeline ships faster and costs less to run 

• Choose real time streaming when the product's value depends on seeing text live, such as a meeting tool where participants read along, and design for the added latency and cost from day one 

• Add speaker diarization only when multiple people talk in most recordings, since it is a real add on cost and adds little value for single speaker use cases like Lexi's 

Accuracy also behaves differently across the two approaches. Batch transcription can reprocess a file if the first pass comes back weak, since nothing is showing on screen yet. Real time transcription has to commit to text as it appears. That makes small errors more visible to the user in the moment, even when the final corrected transcript ends up just as accurate. 

How to Scope the First Version 

Most transcription apps fail on ambition, not on transcription accuracy. A narrow first version proves the core loop before the feature list grows into something nobody can test properly. 

• Pick batch or real time first, since building both from day one roughly doubles the testing surface for very little early value 

• Decide how deep the summary goes, a short recap, key points, or full action items, since each level asks more of the underlying model and needs its own quality check 

• Scope file import carefully, since supporting audio, video, and links like Note Taker's YouTube brief feature each bring a different failure mode to test 

Teams that try to ship live transcription, deep summarization, and broad file import all in version one usually end up cutting one of the three after missing their first deadline. 

What a Realistic Build Timeline Looks Like 

Shipped examples in this category range from two to four months, and four stages make up that window regardless of which end of the range a project lands on. 

• Discovery and scope, where the team locks batch versus real time, the summary depth, and the folder or organization model before any screen gets designed 

• Core build, the largest block on the calendar, where recording, the transcription call, and the summary layer come together against that locked scope 

• Accuracy and edge case testing, run against real recordings with background noise, accents, and multiple speakers, not just clean studio audio 

• Launch and monitor, the first few weeks live, when real users upload the messy, real world audio a test environment rarely captures 

Skipping the accuracy testing stage is the most common reason a demo that sounded perfect in a quiet room turns into a support inbox full of bad transcript complaints. Testing against a handful of clean, single speaker clips tells a team almost nothing. Real signal comes from a crowded call or a lecture hall with poor acoustics, not a quiet office.