Dated measurements — method, numbers, and limits in the same place.
Across 955 jobs on 18 public repositories (13,954 files, 2.7 million lines, 11 languages), a file-by-file agent workflow opened 1 file and read 1,413 lines — 13,321 tokens — for a typical job. The same job answered through Context Zero Engine's code graph used 2,221 tokens in one MCP call: an 83% reduction, 10.1× fewer tokens pooled across the run.
The engine Lven Instant ships runs at RTF 0.166 on a consumer-grade CPU, with a 3.76% word error rate over 73 clean-speech clips. whisper-large-v3 never reached realtime: RTF 1.14 on the GPU and 2.46 on the CPU, 10 to 30 times slower per clip. It was one point more accurate on the 24 clips both engines ran.
Lven Instant runs a deterministic pass over each transcript before it types it: stutter and hyphen-fragment pruning, repeat collapse, filler purge, contraction and proper-noun restoration, sentence casing and terminal punctuation. No language model, no network; the same input always yields the same output. Over the polisher's 31-case golden corpus: 154 words in, 133 out, zero inserted, one spelling restoration.